https://www.slicer.org/w/api.php?action=feedcontributions&feedformat=atom&user=Liangjiazhu 2026-04-05T18:36:57Z User contributions MediaWiki 1.33.0 https://www.slicer.org/w/index.php?title=Documentation/4.4/Modules/FastGrowCut&diff=41284 2015-04-05T20:19:32Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Multi-Label Segmentation Examples}}<br /> The module supports multi-label segmentations. Two examples are shown below.<br /> <br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Interactive Medical Image Computing Workshop, 2014.<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=41283 2015-04-05T20:18:52Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Multi-Label Segmentation Examples}}<br /> The module supports multi-label segmentations. Two examples are shown below.<br /> <br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Interactive Medical Image Computing Workshop, 2014.<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FastGrowCutEffect.png&diff=37749 2014-04-30T15:33:34Z

<p>Liangjiazhu: uploaded a new version of &quot;File:FastGrowCutEffect.png&quot;</p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FastGrowCutScreenShot.png&diff=37693 2014-04-25T20:46:03Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/4.3/Modules/FastGrowCut&diff=37689 2014-04-25T20:32:54Z

<p>Liangjiazhu: Created page with '&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt; &lt;!-- ---------------------------- --&gt; {{documentation/{{documentation/version}}/module-header}} &lt;!-- -----------------------…'</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Multi-Label Segmentation Examples}}<br /> The module supports multi-label segmentations. Two examples are shown below.<br /> <br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37661 2014-04-24T23:41:14Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Multi-Label Segmentation Examples}}<br /> The module supports multi-label segmentations. Two examples are shown below.<br /> <br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37660 2014-04-24T23:39:44Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Multi-Label Segmentation Examples}}<br /> The module supports multi-label segmentations. Two examples are shown below.<br /> <br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37659 2014-04-24T23:36:31Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Segmentation Examples}}<br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> 1) Seed image<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> 2) Segmentation results<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37658 2014-04-24T23:35:45Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Segmentation Examples}}<br /> * Brain ventricle and tumor segmentation<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> 1) Seed image<br /> [[File:FGC_Brain_Seg.png|500px]]<br /> 2) Segmentation results<br /> <br /> * Heart chamber segmentation<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> 1) Seed image<br /> [[File:FGC_Heart_Seg.png|500px]]<br /> 2) Segmentation results<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37657 2014-04-24T23:33:58Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Segmentation Examples}}<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> <br /> [[File:FGC_Brain_Seg.png|500px]]<br /> <br /> [[File:FGC_Heart_Seed.png|500px]]<br /> <br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37656 2014-04-24T23:33:22Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Segmentation Examples}}<br /> [[File:FGC_Brain_Seed.png|500px]]<br /> [[File:FGC_Brain_Seg.png|500px]]<br /> [[File:FGC_Heart_Seed.png|500px]]<br /> [[File:FGC_Heart_Seg.png|500px]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGC_Heart_Seg.png&diff=37655 2014-04-24T23:31:43Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGC_Heart_Seed.png&diff=37654 2014-04-24T23:31:26Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGC_Brain_Seg.png&diff=37653 2014-04-24T23:31:07Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGC_Brain_Seed.png&diff=37652 2014-04-24T23:30:53Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37651 2014-04-24T23:28:19Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37650 2014-04-24T23:27:44Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop,submitted.<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/4.3/Modules&diff=37649 2014-04-24T23:21:22Z

<p>Liangjiazhu: /* Segmentation */</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> =Modules by Category=<br /> ==Core Modules==<br /> <br /> {{documentation/{{documentation/version}}/Developers/random-image}}<br /> <br /> *[[Documentation/{{documentation/version}}/Modules/Annotations|Annotations]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Colors|Colors]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Data|Data]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/DICOM|DICOM]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> **[[Documentation/{{documentation/version}}/Modules/Editor/WatershedFromMarkers|WatershedFromMarkers]] (Bradley Lowekamp) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Markups|Markups]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/Models|Models]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/SceneViews|SceneViews]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Transforms|Transforms]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ViewControllers|View Controllers]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/Volumes|Volumes]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/VolumeRendering|Volume Rendering]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/SlicerWelcome|Welcome to Slicer]] (Wendy Plesniak)<br /> <br /> ==Wizards==<br /> <br /> ==Informatics==<br /> *[[Documentation/{{documentation/version}}/Modules/SampleData|Sample Data]] (Steve Pieper)<br /> <br /> ==Registration==<br /> *[[:Category:Documentation/{{documentation/version}}/Modules/Registration|Introduction to Registration]] (Dominik Meier)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSFit|General Brainsfit Registration]] (Hans Johnson)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample Image (BRAINS)]] (Hans Johnson)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/ACPCTransform|ACPC Transform]] (Nicole Aucoin)<br /> **[[Documentation/{{documentation/version}}/Modules/BRAINSDemonWarp|Demon Registration (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/FiducialRegistration|Fiducial Registration]] (Stephen Aylward) {{wip}}<br /> **Vector Demon Registration (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/Reformat|Reformat]] (Michael Jeulin-Lagarrigue)<br /> **[[Documentation/{{documentation/version}}/Modules/SegmentationAidedRegistration|Segmentation Aided Registration]] (Yi Gao) {{wip}}<br /> <br /> ==Segmentation==<br /> *[[Documentation/{{documentation/version}}/Modules/EMSegment Easy|EMSegment Easy (no atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/EMSegmenter|EMSegment (with atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]] (Jim Miller, Harini Veeraraghavan) <br /> *[[Documentation/{{documentation/version}}/Modules/KSliceInteractiveSegmenter|KSlice Interactive Segmenter]] (Ivan Kolesov, Peter Karasev, Patricio Vela, Allen Tannenbaum, Karl Fritscher, Steve Pieper )<br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/EMSegment Command-line | EMSegment Command-line]] (Kilian Pohl)<br /> **[[Documentation/{{documentation/version}}/Modules/ForegroundMasking|Foreground Masking (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/RobustStatisticsSegmenter|Robust Statistics Segmenter]] (Yi Gao)<br /> **[[Documentation/{{documentation/version}}/Modules/LAScarSegmenter|Left Atrial Scar Segmenter]] (Liangjia Zhu) <br /> **[[Documentation/{{documentation/version}}/Modules/LASegmenter|Left Atrium Segmenter]] (Liangjia Zhu)<br /> **[[Documentation/{{documentation/version}}/Modules/FastGrowCut|Fast GrowCut Effect]] (Liangjia Zhu)<br /> <br /> ==Quantification==<br /> *[[Documentation/{{documentation/version}}/Modules/DataProbe|Data Probe]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/LabelStatistics|Label Statistics]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/PETStandardUptakeValueComputation|PET Standard Uptake Value Computation]] (Nicole Aucoin)<br /> * Change quantification<br /> ** [[Documentation/{{documentation/version}}/Modules/IntensityDifferenceMetric|Intensity Difference Metric]] (Andrey Fedorov)<br /> <br /> ==Diffusion== <br /> *DWI to Full Brain Tractography (Demian Wasserman) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/DWIJointRicianLMMSEFilter|DWI Joint Rician LMMSE Image Filter]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DWIRicianLMMSEFilter|DWI Rician LMMSE Filter]] (Demian Wasserman) {{wip}}<br /> *Tractography<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyDisplay|Tractography Display]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyFiducialSeeding|Tractography Interactive Seeding]] (Alex Yarmarkovich)<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyLabelMapSeeding|Tractography Label Map Seeding]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/UKFTractography|UKF Tractography]]<br /> *Utilities<br /> **[[Documentation/{{documentation/version}}/Modules/DWIToDTIEstimation|DWI To DTI Estimation]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]] (Demian Wasserman) <br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionWeightedVolumeMasking|Diffusion Weighted Volume Masking]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleLabelSelect|FiberBundleLabelSelect]] (Alex Yarmarkovich) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleDTIVolume|Resample DTI Volume]] (Demian Wasserman) {{wip}}<br /> *Data Conversion<br /> **[[Documentation/{{documentation/version}}/Modules/DTIImport|DTIImport]] {{wip}} (Sonia Pujol) <br /> **[[Documentation/{{documentation/version}}/Modules/DTIExport|DTIExport]] {{wip}} (Sonia Pujol)<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleToLabelMap|Fiber Bundle to Label Map]] (Steve Pieper)<br /> <br /> ==IGT==<br /> *[[Documentation/{{documentation/version}}/Modules/OpenIGTLinkIF| OpenIGTLink IF]] (Junichi Tokuda) {{wip}}<br /> <br /> ==Filtering==<br /> *[[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection | N4ITK Bias Field Correction]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/CheckerBoardFilter|CheckerBoard Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExtractSkeleton|Extract Skeleton]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/HistogramMatching|Histogram Matching]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ImageLabelCombine|Image Label Combine]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ResampleScalarVectorDWIVolume|Resample Scalar/Vector/DWI Volume]] (Francois Budin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ThresholdScalarVolume|Threshold Scalar Volume]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/VotingBinaryHoleFillingImageFilter|Voting Binary Hole Filling Image Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SimpleFilters|Simple Filters]] (Bradley Lowekamp) {{wip}}<br /> *Arithmetic <br /> **[[Documentation/{{documentation/version}}/Modules/AddScalarVolumes|Add Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/CastScalarVolume| Cast Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MaskScalarVolume| Mask Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MultiplyScalarVolumes| Multiply Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/SubtractScalarVolumes| Subtract Scalar Volumes]] (Jim Miller) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion |Gradient Anisotropic Diffusion]] (Jim Miller) {{wip}} <br /> **[[Documentation/{{documentation/version}}/Modules/CurvatureAnisotropicDiffusion|Curvature Anisotropic Diffusion]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/GaussianBlurImageFilter|Gaussian Blur Image Filter]] (Stephen Aylward) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MedianImageFilter|Median Image Filter]] (Jim Miller) {{wip}}<br /> *Morphology<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleFillHoleImageFilter|Grayscale Fill Hole Image Filter]] (Jim Miller)<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleGrindPeakImageFilter|Grayscale Grind Peak Image Filter]] (Jim Miller) {{wip}}<br /> <br /> ==Surface Models==<br /> *[[Documentation/{{documentation/version}}/Modules/GrayscaleModelMaker| Grayscale Model Maker]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/LabelMapSmoothing|Label Map Smoothing]] {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/MergeModels| Merge Models]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelMaker| Model Maker]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelToLabelMap| Model To LabelMap]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ProbeVolumeWithModel| Probe Volume With Model]] (Lauren O'Donnell) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SurfaceToolbox| SurfaceToolbox]] (Luca Antiga)<br /> <br /> ==Converters==<br /> *[[Documentation/{{documentation/version}}/Modules/CreateDICOMSeries|Create DICOM Series]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Crop Volume|Crop Volume]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DicomToNrrdConverter|Dicom to Nrrd Converter]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/OrientScalarVolume| Orient Scalar Volume]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/VectorToScalarVolume| Vector to Scalar Volume]] (Steve Pieper) <br /> {{wip}}<br /> <br /> ==Endoscopy==<br /> *[[Documentation/{{documentation/version}}/Modules/Endoscopy|Endoscopy]] (Steve Pieper)<br /> <br /> =={{wip}} Work in Progress {{wip}}==<br /> * MultiVolume Support<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeImporter|MultiVolume Importer]] (Andrey Fedorov)<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeExplorer|MultiVolume Explorer]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DataStore|Data Store]] (Charles Marion)<br /> <br /> ==Developer Tools==<br /> *[[Documentation/{{documentation/version}}/Modules/Cameras|Cameras]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/EventBroker|Event Broker]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExecutionModelTour |Execution Model Tour]] (JC Fillion-Robin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/YOURMODULENAME|Module Template]] (JC Fillion-Robin)<br /> *Performance Tests<br /> *Tractography<br /> <br /> ==Legacy==<br /> *Converters<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineToDeformationField|BSpline To Deformation Field]]<br /> *Diffusion<br /> **Denoising<br /> ***[[Documentation/{{documentation/version}}/Modules/DWIUnbiasedNonLocalMeansFilter|DWI Unbiased Non Local Means Filter]]<br /> *Filtering<br /> **[[Documentation/{{documentation/version}}/Modules/MRIBiasFieldCorrection|MRI Bias Field Correction]] (Sylvain Jaume) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdImageFilter|Otsu Threshold Image Filter]] (Bill Lorensen) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleScalarVolume|Resample Scalar Volume]] (Jim Miller) {{wip}}<br /> *Registration<br /> **[[Documentation/{{documentation/version}}/Modules/RigidRegistration|Rigid Registration]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/AffineRegistration|Affine Registration]] (Daniel Blezek) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineDeformableRegistration|BSpline Deformable Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ExpertAutomatedRegistration|Expert Automated Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/LinearRegistration|Linear Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MultiResolutionAffineRegistration|MultiResolution Affine Registration]] (Casey Goodlett){{wip}}<br /> &lt;!--<br /> **[[Documentation/{{documentation/version}}/Modules/TestGridTransformRegistration|Test Grid Transform Registration]] {{wip}}<br /> --&gt;<br /> *Segmentation<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdSegmentation|Otsu Threshold Segmentation]] (Bill Lorensen) {{wip}}</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37648 2014-04-24T23:20:06Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:FastGrowCutEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FastGrowCutEffect.png&diff=37647 2014-04-24T23:19:02Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37635 2014-04-18T17:16:55Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37634 2014-04-18T17:16:12Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Modules/SlicerModuleCardiacRegistration]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> * Y Gao, Y Rathi, S Bouix, A Tannenbaum; Filtering in the Diffeomorphism Group and the Registration of Point Sets; IEEE Transactions on Image Processing 21 (10), 4383--4396<br /> * Y Gao, B Gholami, RS MacLeod, J Blauer, WM Haddad, A Tannenbaum; [http://www.na-mic.org/publications/item/view/1844 Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning.] Proceedings of SPIE Medical Imaging 2010.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules&diff=37633 2014-04-18T17:14:57Z

<p>Liangjiazhu: /* Segmentation */</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> =Modules by Category=<br /> ==Core Modules==<br /> <br /> {{documentation/{{documentation/version}}/Developers/random-image}}<br /> <br /> *[[Documentation/{{documentation/version}}/Modules/Annotations|Annotations]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Colors|Colors]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Data|Data]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/DICOM|DICOM]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> **[[Documentation/{{documentation/version}}/Modules/Editor/WatershedFromMarkers|WatershedFromMarkers]] (Bradley Lowekamp) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Markups|Markups]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/Models|Models]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/SceneViews|SceneViews]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Transforms|Transforms]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ViewControllers|View Controllers]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/Volumes|Volumes]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/VolumeRendering|Volume Rendering]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/SlicerWelcome|Welcome to Slicer]] (Wendy Plesniak)<br /> <br /> ==Wizards==<br /> <br /> ==Informatics==<br /> *[[Documentation/{{documentation/version}}/Modules/DataStore|Data Store]] (Charles Marion)<br /> *[[Documentation/{{documentation/version}}/Modules/SampleData|Sample Data]] (Steve Pieper)<br /> <br /> ==Registration==<br /> *[[:Category:Documentation/{{documentation/version}}/Modules/Registration|Introduction to Registration]] (Dominik Meier)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSFit|General Brainsfit Registration]] (Hans Johnson)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample Image (BRAINS)]] (Hans Johnson)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/ACPCTransform|ACPC Transform]] (Nicole Aucoin)<br /> **[[Documentation/{{documentation/version}}/Modules/BRAINSDemonWarp|Demon Registration (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/FiducialRegistration|Fiducial Registration]] (Stephen Aylward) {{wip}}<br /> **Vector Demon Registration (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/Reformat|Reformat]] (Michael Jeulin-Lagarrigue)<br /> **[[Documentation/{{documentation/version}}/Modules/SegmentationAidedRegistration|Segmentation Aided Registration]] (Yi Gao) {{wip}}<br /> <br /> ==Segmentation==<br /> *[[Documentation/{{documentation/version}}/Modules/EMSegment Easy|EMSegment Easy (no atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/EMSegmenter|EMSegment (with atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]] (Jim Miller, Harini Veeraraghavan) <br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/EMSegment Command-line | EMSegment Command-line]] (Kilian Pohl)<br /> **[[Documentation/{{documentation/version}}/Modules/ForegroundMasking|Foreground Masking (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/RobustStatisticsSegmenter|Robust Statistics Segmenter]] (Yi Gao)<br /> **[[Documentation/{{documentation/version}}/Modules/LAScarSegmenter|Left Atrial Scar Segmenter]] (Liangjia Zhu)<br /> **[[Documentation/{{documentation/version}}/Modules/LASegmenter|Left Atrium Segmenter]] (Liangjia Zhu)<br /> **[[Documentation/{{documentation/version}}/Modules/FastGrowCut|Fast GrowCut]] (Liangjia Zhu) {{wip}}<br /> <br /> ==Quantification==<br /> *[[Documentation/{{documentation/version}}/Modules/DataProbe|Data Probe]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/LabelStatistics|Label Statistics]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/PETStandardUptakeValueComputation|PET Standard Uptake Value Computation]] (Nicole Aucoin)<br /> <br /> ==Diffusion== <br /> *DWI to Full Brain Tractography (Demian Wasserman) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/DWIJointRicianLMMSEFilter|DWI Joint Rician LMMSE Image Filter]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DWIRicianLMMSEFilter|DWI Rician LMMSE Filter]] (Demian Wasserman) {{wip}}<br /> *Tractography<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyDisplay|Tractography Display]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyFiducialSeeding|Tractography Interactive Seeding]] (Alex Yarmarkovich)<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyLabelMapSeeding|Tractography Label Map Seeding]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/UKFTractography|UKF Tractography]]<br /> *Utilities<br /> **[[Documentation/{{documentation/version}}/Modules/DWIToDTIEstimation|DWI To DTI Estimation]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]] (Demian Wasserman) <br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionWeightedVolumeMasking|Diffusion Weighted Volume Masking]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleDTIVolume|Resample DTI Volume]] (Demian Wasserman) {{wip}}<br /> *Data Conversion<br /> **[[Documentation/{{documentation/version}}/Modules/DTIImport|DTIImport]] {{wip}} (Sonia Pujol) <br /> **[[Documentation/{{documentation/version}}/Modules/DTIExport|DTIExport]] {{wip}} (Sonia Pujol)<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleToLabelMap|Fiber Bundle to Label Map]] (Steve Pieper)<br /> <br /> ==IGT==<br /> *[[Documentation/{{documentation/version}}/Modules/OpenIGTLinkIF| OpenIGTLink IF]] (Junichi Tokuda) {{wip}}<br /> <br /> ==Filtering==<br /> *[[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection | N4ITK Bias Field Correction]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/CheckerBoardFilter|CheckerBoard Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExtractSkeleton|Extract Skeleton]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/HistogramMatching|Histogram Matching]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ImageLabelCombine|Image Label Combine]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ResampleScalarVectorDWIVolume|Resample Scalar/Vector/DWI Volume]] (Francois Budin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ThresholdScalarVolume|Threshold Scalar Volume]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/VotingBinaryHoleFillingImageFilter|Voting Binary Hole Filling Image Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SimpleFilters|Simple Filters]] (Bradley Lowekamp) {{wip}}<br /> *Arithmetic <br /> **[[Documentation/{{documentation/version}}/Modules/AddScalarVolumes|Add Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/CastScalarVolume| Cast Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MaskScalarVolume| Mask Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MultiplyScalarVolumes| Multiply Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/SubtractScalarVolumes| Subtract Scalar Volumes]] (Jim Miller) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion |Gradient Anisotropic Diffusion]] (Jim Miller) {{wip}} <br /> **[[Documentation/{{documentation/version}}/Modules/CurvatureAnisotropicDiffusion|Curvature Anisotropic Diffusion]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/GaussianBlurImageFilter|Gaussian Blur Image Filter]] (Stephen Aylward) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MedianImageFilter|Median Image Filter]] (Jim Miller) {{wip}}<br /> *Morphology<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleFillHoleImageFilter|Grayscale Fill Hole Image Filter]] (Jim Miller)<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleGrindPeakImageFilter|Grayscale Grind Peak Image Filter]] (Jim Miller) {{wip}}<br /> <br /> ==Surface Models==<br /> *[[Documentation/{{documentation/version}}/Modules/GrayscaleModelMaker| Grayscale Model Maker]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/LabelMapSmoothing|Label Map Smoothing]] {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/MergeModels| Merge Models]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelMaker| Model Maker]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelToLabelMap| Model To LabelMap]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ProbeVolumeWithModel| Probe Volume With Model]] (Lauren O'Donnell) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SurfaceToolbox| SurfaceToolbox]] (Luca Antiga)<br /> <br /> ==Converters==<br /> *[[Documentation/{{documentation/version}}/Modules/CreateDICOMSeries|Create DICOM Series]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Crop Volume|Crop Volume]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DicomToNrrdConverter|Dicom to Nrrd Converter]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/OrientScalarVolume| Orient Scalar Volume]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/VectorToScalarVolume| Vector to Scalar Volume]] (Steve Pieper) <br /> {{wip}}<br /> <br /> ==Endoscopy==<br /> *[[Documentation/{{documentation/version}}/Modules/Endoscopy|Endoscopy]] (Steve Pieper)<br /> <br /> =={{wip}} Work in Progress {{wip}}==<br /> * MultiVolume Support<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeImporter|MultiVolume Importer]] (Andrey Fedorov)<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeExplorer|MultiVolume Explorer]] (Andrey Fedorov)<br /> <br /> ==Developer Tools==<br /> *[[Documentation/{{documentation/version}}/Modules/Cameras|Cameras]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/EventBroker|Event Broker]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExecutionModelTour |Execution Model Tour]] (JC Fillion-Robin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/YOURMODULENAME|Module Template]] (JC Fillion-Robin)<br /> *Performance Tests<br /> *Tractography<br /> <br /> ==Legacy==<br /> *Converters<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineToDeformationField|BSpline To Deformation Field]]<br /> *Diffusion<br /> **Denoising<br /> ***[[Documentation/{{documentation/version}}/Modules/DWIUnbiasedNonLocalMeansFilter|DWI Unbiased Non Local Means Filter]]<br /> *Filtering<br /> **[[Documentation/{{documentation/version}}/Modules/MRIBiasFieldCorrection|MRI Bias Field Correction]] (Sylvain Jaume) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdImageFilter|Otsu Threshold Image Filter]] (Bill Lorensen) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleScalarVolume|Resample Scalar Volume]] (Jim Miller) {{wip}}<br /> *Registration<br /> **[[Documentation/{{documentation/version}}/Modules/RigidRegistration|Rigid Registration]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/AffineRegistration|Affine Registration]] (Daniel Blezek) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineDeformableRegistration|BSpline Deformable Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ExpertAutomatedRegistration|Expert Automated Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/LinearRegistration|Linear Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MultiResolutionAffineRegistration|MultiResolution Affine Registration]] (Casey Goodlett){{wip}}<br /> &lt;!--<br /> **[[Documentation/{{documentation/version}}/Modules/TestGridTransformRegistration|Test Grid Transform Registration]] {{wip}}<br /> --&gt;<br /> *Segmentation<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdSegmentation|Otsu Threshold Segmentation]] (Bill Lorensen) {{wip}}</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37632 2014-04-18T17:13:48Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Contributor: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Contributor: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Contributor: Ron Kikinis, BWH &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> <br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37631 2014-04-18T17:09:24Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FastGrowCut&diff=37630 2014-04-18T17:08:40Z

<p>Liangjiazhu: Created page with '&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt; &lt;!-- ---------------------------- --&gt; {{documentation/{{documentation/version}}/module-header}} &lt;!-- -----------------------…'</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be repeated a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:<br /> * (a) press 'F' for local-global Chan-Vese segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules&diff=37629 2014-04-18T17:03:46Z

<p>Liangjiazhu: /* Segmentation */</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> =Modules by Category=<br /> ==Core Modules==<br /> <br /> {{documentation/{{documentation/version}}/Developers/random-image}}<br /> <br /> *[[Documentation/{{documentation/version}}/Modules/Annotations|Annotations]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Colors|Colors]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Data|Data]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/DICOM|DICOM]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> **[[Documentation/{{documentation/version}}/Modules/Editor/WatershedFromMarkers|WatershedFromMarkers]] (Bradley Lowekamp) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Markups|Markups]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/Models|Models]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/SceneViews|SceneViews]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Transforms|Transforms]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ViewControllers|View Controllers]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/Volumes|Volumes]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/VolumeRendering|Volume Rendering]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/SlicerWelcome|Welcome to Slicer]] (Wendy Plesniak)<br /> <br /> ==Wizards==<br /> <br /> ==Informatics==<br /> *[[Documentation/{{documentation/version}}/Modules/DataStore|Data Store]] (Charles Marion)<br /> *[[Documentation/{{documentation/version}}/Modules/SampleData|Sample Data]] (Steve Pieper)<br /> <br /> ==Registration==<br /> *[[:Category:Documentation/{{documentation/version}}/Modules/Registration|Introduction to Registration]] (Dominik Meier)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSFit|General Brainsfit Registration]] (Hans Johnson)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample Image (BRAINS)]] (Hans Johnson)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/ACPCTransform|ACPC Transform]] (Nicole Aucoin)<br /> **[[Documentation/{{documentation/version}}/Modules/BRAINSDemonWarp|Demon Registration (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/FiducialRegistration|Fiducial Registration]] (Stephen Aylward) {{wip}}<br /> **Vector Demon Registration (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/Reformat|Reformat]] (Michael Jeulin-Lagarrigue)<br /> **[[Documentation/{{documentation/version}}/Modules/SegmentationAidedRegistration|Segmentation Aided Registration]] (Yi Gao) {{wip}}<br /> <br /> ==Segmentation==<br /> *[[Documentation/{{documentation/version}}/Modules/EMSegment Easy|EMSegment Easy (no atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/EMSegmenter|EMSegment (with atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]] (Jim Miller, Harini Veeraraghavan) <br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/EMSegment Command-line | EMSegment Command-line]] (Kilian Pohl)<br /> **[[Documentation/{{documentation/version}}/Modules/ForegroundMasking|Foreground Masking (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/RobustStatisticsSegmenter|Robust Statistics Segmenter]] (Yi Gao)<br /> **[[Documentation/{{documentation/version}}/Modules/LAScarSegmenter|Left Atrial Scar Segmenter]] (Liangjia Zhu)<br /> **[[Documentation/{{documentation/version}}/Modules/LASegmenter|Left Atrium Segmenter]] (Liangjia Zhu)<br /> **[[Documentation/{{documentation/version}}/Modules/FastGrowCut|Fast GrowCut]] (Liangjia Zhu)<br /> <br /> ==Quantification==<br /> *[[Documentation/{{documentation/version}}/Modules/DataProbe|Data Probe]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/LabelStatistics|Label Statistics]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/PETStandardUptakeValueComputation|PET Standard Uptake Value Computation]] (Nicole Aucoin)<br /> <br /> ==Diffusion== <br /> *DWI to Full Brain Tractography (Demian Wasserman) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/DWIJointRicianLMMSEFilter|DWI Joint Rician LMMSE Image Filter]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DWIRicianLMMSEFilter|DWI Rician LMMSE Filter]] (Demian Wasserman) {{wip}}<br /> *Tractography<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyDisplay|Tractography Display]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyFiducialSeeding|Tractography Interactive Seeding]] (Alex Yarmarkovich)<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyLabelMapSeeding|Tractography Label Map Seeding]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/UKFTractography|UKF Tractography]]<br /> *Utilities<br /> **[[Documentation/{{documentation/version}}/Modules/DWIToDTIEstimation|DWI To DTI Estimation]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]] (Demian Wasserman) <br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionWeightedVolumeMasking|Diffusion Weighted Volume Masking]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleDTIVolume|Resample DTI Volume]] (Demian Wasserman) {{wip}}<br /> *Data Conversion<br /> **[[Documentation/{{documentation/version}}/Modules/DTIImport|DTIImport]] {{wip}} (Sonia Pujol) <br /> **[[Documentation/{{documentation/version}}/Modules/DTIExport|DTIExport]] {{wip}} (Sonia Pujol)<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleToLabelMap|Fiber Bundle to Label Map]] (Steve Pieper)<br /> <br /> ==IGT==<br /> *[[Documentation/{{documentation/version}}/Modules/OpenIGTLinkIF| OpenIGTLink IF]] (Junichi Tokuda) {{wip}}<br /> <br /> ==Filtering==<br /> *[[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection | N4ITK Bias Field Correction]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/CheckerBoardFilter|CheckerBoard Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExtractSkeleton|Extract Skeleton]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/HistogramMatching|Histogram Matching]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ImageLabelCombine|Image Label Combine]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ResampleScalarVectorDWIVolume|Resample Scalar/Vector/DWI Volume]] (Francois Budin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ThresholdScalarVolume|Threshold Scalar Volume]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/VotingBinaryHoleFillingImageFilter|Voting Binary Hole Filling Image Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SimpleFilters|Simple Filters]] (Bradley Lowekamp) {{wip}}<br /> *Arithmetic <br /> **[[Documentation/{{documentation/version}}/Modules/AddScalarVolumes|Add Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/CastScalarVolume| Cast Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MaskScalarVolume| Mask Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MultiplyScalarVolumes| Multiply Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/SubtractScalarVolumes| Subtract Scalar Volumes]] (Jim Miller) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion |Gradient Anisotropic Diffusion]] (Jim Miller) {{wip}} <br /> **[[Documentation/{{documentation/version}}/Modules/CurvatureAnisotropicDiffusion|Curvature Anisotropic Diffusion]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/GaussianBlurImageFilter|Gaussian Blur Image Filter]] (Stephen Aylward) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MedianImageFilter|Median Image Filter]] (Jim Miller) {{wip}}<br /> *Morphology<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleFillHoleImageFilter|Grayscale Fill Hole Image Filter]] (Jim Miller)<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleGrindPeakImageFilter|Grayscale Grind Peak Image Filter]] (Jim Miller) {{wip}}<br /> <br /> ==Surface Models==<br /> *[[Documentation/{{documentation/version}}/Modules/GrayscaleModelMaker| Grayscale Model Maker]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/LabelMapSmoothing|Label Map Smoothing]] {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/MergeModels| Merge Models]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelMaker| Model Maker]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelToLabelMap| Model To LabelMap]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ProbeVolumeWithModel| Probe Volume With Model]] (Lauren O'Donnell) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SurfaceToolbox| SurfaceToolbox]] (Luca Antiga)<br /> <br /> ==Converters==<br /> *[[Documentation/{{documentation/version}}/Modules/CreateDICOMSeries|Create DICOM Series]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Crop Volume|Crop Volume]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DicomToNrrdConverter|Dicom to Nrrd Converter]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/OrientScalarVolume| Orient Scalar Volume]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/VectorToScalarVolume| Vector to Scalar Volume]] (Steve Pieper) <br /> {{wip}}<br /> <br /> ==Endoscopy==<br /> *[[Documentation/{{documentation/version}}/Modules/Endoscopy|Endoscopy]] (Steve Pieper)<br /> <br /> =={{wip}} Work in Progress {{wip}}==<br /> * MultiVolume Support<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeImporter|MultiVolume Importer]] (Andrey Fedorov)<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeExplorer|MultiVolume Explorer]] (Andrey Fedorov)<br /> <br /> ==Developer Tools==<br /> *[[Documentation/{{documentation/version}}/Modules/Cameras|Cameras]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/EventBroker|Event Broker]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExecutionModelTour |Execution Model Tour]] (JC Fillion-Robin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/YOURMODULENAME|Module Template]] (JC Fillion-Robin)<br /> *Performance Tests<br /> *Tractography<br /> <br /> ==Legacy==<br /> *Converters<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineToDeformationField|BSpline To Deformation Field]]<br /> *Diffusion<br /> **Denoising<br /> ***[[Documentation/{{documentation/version}}/Modules/DWIUnbiasedNonLocalMeansFilter|DWI Unbiased Non Local Means Filter]]<br /> *Filtering<br /> **[[Documentation/{{documentation/version}}/Modules/MRIBiasFieldCorrection|MRI Bias Field Correction]] (Sylvain Jaume) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdImageFilter|Otsu Threshold Image Filter]] (Bill Lorensen) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleScalarVolume|Resample Scalar Volume]] (Jim Miller) {{wip}}<br /> *Registration<br /> **[[Documentation/{{documentation/version}}/Modules/RigidRegistration|Rigid Registration]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/AffineRegistration|Affine Registration]] (Daniel Blezek) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineDeformableRegistration|BSpline Deformable Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ExpertAutomatedRegistration|Expert Automated Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/LinearRegistration|Linear Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MultiResolutionAffineRegistration|MultiResolution Affine Registration]] (Casey Goodlett){{wip}}<br /> &lt;!--<br /> **[[Documentation/{{documentation/version}}/Modules/TestGridTransformRegistration|Test Grid Transform Registration]] {{wip}}<br /> --&gt;<br /> *Segmentation<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdSegmentation|Otsu Threshold Segmentation]] (Bill Lorensen) {{wip}}</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/CarreraSliceInteractiveSegmenter&diff=37091 2014-01-23T16:28:35Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is the CarreraSlice interactive segmentation method. The computation is initialized using an improved GrowCut implementation and refined by an interactive, level set based segmentation algorithm, KSlice. As the automatic segmentation runs and displays the current state, a human user can influence the algorithm’s behavior to more closely align with an expected result.<br /> In the end, the user has created an excellent segmentation result with a minimal amount of time and effort. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finish editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be conducted a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to work with KSlice for smoothing. The energe functionals available include:<br /> * (a) press 'F' for local-global segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> == As necessary, take steps at any time in the process ==<br /> <br /> *(Used in any order) A segmenting surface may leak into undesirable areas, based purely on image data. It is the user's job to guide the evolution process. A user can provide positive input (drawing) by selecting a paint tool, painting in areas which should be part of the object of interest but are not. Or, the user can provide negative input (erasing) by pressing &quot;A&quot; (toggle label value between 0 and the originally selected label). An example of a user correction is going from <br /> <br /> [[File:KSliceNegativeInput.png|500px]]<br /> <br /> to<br /> <br /> [[File:KSliceCorrectedUI.png|500px]]<br /> <br /> * (Used in any order) Certain image regions (depending on the energy chosen and the image itself) may be highly attractive for the evolving contour despite being incorrect according to the user's expert knowledge. He has two options. First, apply repeated input several times (soft user constraints). Second, he can press &quot;B&quot; (toggle between soft/hard constraints) to increase the weight on his inputs, essentially making them hard constraints. Further, constraints can be provided pre-emptively, by erasing/drawing in regions before a contour has reached these areas to prevent leaks in the future or expedite the evolution process, respectively. <br /> <br /> <br /> *(Used in any order) Press &quot;F&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the Local Global Chan-Vese. The user can switch the energy he is using at any time.<br /> <br /> * (Used in any order) Press &quot;U&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the curvature flow. The user can switch the energy he is using at any time.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> <br /> == Optional Visualization ==<br /> <br /> * (Optional) Turn on “Volume Rendering” of the label image in 3D (notice the parameters set for faster interaction). This will be updated during the evolution<br /> <br /> [[File:VolumeRederingOnMeningioma.png|500px]]<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> * Examine the current segmentation by flipping through the 2D viewers, providing input as in Step.12 to guide the evolution. <br /> <br /> [[File:ExampleUIDuringProcess.png|500px]] <br /> <br /> <br /> <br /> * Repeat previous steps (or any subset of them) until convergence to the desired segmentation.<br /> <br /> [[File:KSliceFinalSegMeningioma.png|500px]] <br /> <br /> <br /> *Once satisfied with the segmentation, stop CarreraSlice by clicking &quot;Stop Interactive Segmentor&quot;<br /> <br /> [[File:StopInteractiveSegmenter.png|500px]]<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> There are two parameters to set: the radius that sets the band around the current initialisation in which image data is considered (for Local-Global Chan-Vese energy) and the number of contour iterations. A large radius means data from far lying regions influences the segmentation (this increases the computational complexity). A small radius ensures that only nearby pixels influence the contour (but set too small will require a better contour initialisation from the user). The number of iterations refers to the time for which the segmenting surface is allowed to evolve before querying the user for input. A small number allows the user to catch the surface before a large error is made, reducing corrective inputs required but may be tedious/slow.<br /> <br /> <br /> <br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-parametersdescription}}--&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> *Kolesov, I., Karasev, P., Muller, G., Chudy, K., Xerogeanes, J., and Tannenbaum, A., “Human supervisory control framework for interactive medical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Biomechanics for Medicine Workshop, Proceeding of the, 2011.<br /> <br /> *Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., and Tannenbaum, A., “Interactive mri segmentation with controlled active vision,” in IEEE Conference on Decision and Control, Proceedings of the, IEEE, Dec. 2011.<br /> <br /> *Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., and Tannenbaum, A.. “Interactive Medical Image Segmentation using PDE Control Of Active Contours”. In: Medical Imaging, IEEE Transactions on (In Submission) (2013)<br /> <br /> *Xerogeanes, J., Mitchell, P. , Karasev, P., Kolesov, I. and Romine, S. . “An Anatomic and Morphologic Evaluation of the Quadriceps Tendon Using 3D MRI Reconstruction: Applications for ACL Autograft Choice and Procurement”. In: Journal of Bone and Joint Surgery (In Submission) (2013)<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/CarreraSliceInteractiveSegmenter&diff=37025 2014-01-16T04:24:17Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: Liangjia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is the CarreraSlice interactive segmentation method. The computation is initialized using an improved GrowCut implementation and refined by an interactive, level set based segmentation algorithm, KSlice. As the automatic segmentation runs and displays the current state, a human user can influence the algorithm’s behavior to more closely align with an expected result.<br /> In the end, the user has created an excellent segmentation result with a minimal amount of time and effort. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finish editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be conducted a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to work with KSlice for smoothing. The energe functionals available include:<br /> * (a) press 'F' for local-global segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> Step 8.) Select the rectangle tool(or any other painting tool).<br /> <br /> [[File:ImplicitRectangle.png|50px]]<br /> <br /> and initialize CarreraSlice in a 2D slice by drawing inside the structure of interest.<br /> <br /> [[File:KSliceInitMeningioma.png|500px]]<br /> <br /> <br /> Step 9.) Press the “C” key to copy the current initialization in this 2D plane, then press the left/right arrow on the keyboard to move on to an adjacent slice, press the “V” key to paste the label, thereby initializing the current slice. Once this procedure has been repeated several times, we have initialized the surface to a cube in 3D.<br /> <br /> [[File:KSliceInit3DCube.png|500px]]<br /> <br /> <br /> Step 10.) To evolve the 2D contour on a slice, hover the mouse over a 2D view plane, press “Q”.<br /> <br /> [[File:KSlice2DEvolve.png|500px]]<br /> <br /> <br /> <br /> Step 11.) Evolve the contour in 3D by pressing &quot;E&quot; to use the Chan-Vese energy.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> == As necessary, take steps at any time in the process ==<br /> <br /> Step 12.)(Used in any order) A segmenting surface may leak into undesirable areas, based purely on image data. It is the user's job to guide the evolution process. A user can provide positive input (drawing) by selecting a paint tool, painting in areas which should be part of the object of interest but are not. Or, the user can provide negative input (erasing) by pressing &quot;A&quot; (toggle label value between 0 and the originally selected label). An example of a user correction is going from <br /> <br /> [[File:KSliceNegativeInput.png|500px]]<br /> <br /> to<br /> <br /> [[File:KSliceCorrectedUI.png|500px]]<br /> <br /> Step 13.) (Used in any order) Certain image regions (depending on the energy chosen and the image itself) may be highly attractive for the evolving contour despite being incorrect according to the user's expert knowledge. He has two options. First, apply repeated input several times (soft user constraints). Second, he can press &quot;B&quot; (toggle between soft/hard constraints) to increase the weight on his inputs, essentially making them hard constraints. Further, constraints can be provided pre-emptively, by erasing/drawing in regions before a contour has reached these areas to prevent leaks in the future or expedite the evolution process, respectively. <br /> <br /> <br /> Step 14.)(Used in any order) Press &quot;F&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the Local Global Chan-Vese. The user can switch the energy he is using at any time.<br /> <br /> Step 15.) (Used in any order) Press &quot;U&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the curvature flow. The user can switch the energy he is using at any time.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> <br /> == Optional Visualization ==<br /> <br /> Step 16.) (Optional) Turn on “Volume Rendering” of the label image in 3D (notice the parameters set for faster interaction). This will be updated during the evolution<br /> <br /> [[File:VolumeRederingOnMeningioma.png|500px]]<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> Step 17.) Examine the current segmentation by flipping through the 2D viewers, providing input as in Step.12 to guide the evolution. <br /> <br /> [[File:ExampleUIDuringProcess.png|500px]] <br /> <br /> <br /> <br /> Step 18.) Repeat steps 10-15 (or any subset of them) until convergence to the desired segmentation.<br /> <br /> [[File:KSliceFinalSegMeningioma.png|500px]] <br /> <br /> <br /> Step 19.) Once satisfied with the segmentation, stop CarreraSlice by clicking &quot;Stop Interactive Segmentor&quot;<br /> <br /> [[File:StopInteractiveSegmenter.png|500px]]<br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> There are two parameters to set: the radius that sets the band around the current initialisation in which image data is considered (for Local-Global Chan-Vese energy) and the number of contour iterations. A large radius means data from far lying regions influences the segmentation (this increases the computational complexity). A small radius ensures that only nearby pixels influence the contour (but set too small will require a better contour initialisation from the user). The number of iterations refers to the time for which the segmenting surface is allowed to evolve before querying the user for input. A small number allows the user to catch the surface before a large error is made, reducing corrective inputs required but may be tedious/slow.<br /> <br /> <br /> <br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-parametersdescription}}--&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> *Kolesov, I., Karasev, P., Muller, G., Chudy, K., Xerogeanes, J., and Tannenbaum, A., “Human supervisory control framework for interactive medical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Biomechanics for Medicine Workshop, Proceeding of the, 2011.<br /> <br /> *Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., and Tannenbaum, A., “Interactive mri segmentation with controlled active vision,” in IEEE Conference on Decision and Control, Proceedings of the, IEEE, Dec. 2011.<br /> <br /> *Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., and Tannenbaum, A.. “Interactive Medical Image Segmentation using PDE Control Of Active Contours”. In: Medical Imaging, IEEE Transactions on (In Submission) (2013)<br /> <br /> *Xerogeanes, J., Mitchell, P. , Karasev, P., Kolesov, I. and Romine, S. . “An Anatomic and Morphologic Evaluation of the Quadriceps Tendon Using 3D MRI Reconstruction: Applications for ACL Autograft Choice and Procurement”. In: Journal of Bone and Joint Surgery (In Submission) (2013)<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/CarreraSliceInteractiveSegmenter&diff=37024 2014-01-16T04:24:03Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: LiangJia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is the CarreraSlice interactive segmentation method. The computation is initialized using an improved GrowCut implementation and refined by an interactive, level set based segmentation algorithm, KSlice. As the automatic segmentation runs and displays the current state, a human user can influence the algorithm’s behavior to more closely align with an expected result.<br /> In the end, the user has created an excellent segmentation result with a minimal amount of time and effort. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finish editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be conducted a couple of times until satisfied.<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to work with KSlice for smoothing. The energe functionals available include:<br /> * (a) press 'F' for local-global segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> Step 8.) Select the rectangle tool(or any other painting tool).<br /> <br /> [[File:ImplicitRectangle.png|50px]]<br /> <br /> and initialize CarreraSlice in a 2D slice by drawing inside the structure of interest.<br /> <br /> [[File:KSliceInitMeningioma.png|500px]]<br /> <br /> <br /> Step 9.) Press the “C” key to copy the current initialization in this 2D plane, then press the left/right arrow on the keyboard to move on to an adjacent slice, press the “V” key to paste the label, thereby initializing the current slice. Once this procedure has been repeated several times, we have initialized the surface to a cube in 3D.<br /> <br /> [[File:KSliceInit3DCube.png|500px]]<br /> <br /> <br /> Step 10.) To evolve the 2D contour on a slice, hover the mouse over a 2D view plane, press “Q”.<br /> <br /> [[File:KSlice2DEvolve.png|500px]]<br /> <br /> <br /> <br /> Step 11.) Evolve the contour in 3D by pressing &quot;E&quot; to use the Chan-Vese energy.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> == As necessary, take steps at any time in the process ==<br /> <br /> Step 12.)(Used in any order) A segmenting surface may leak into undesirable areas, based purely on image data. It is the user's job to guide the evolution process. A user can provide positive input (drawing) by selecting a paint tool, painting in areas which should be part of the object of interest but are not. Or, the user can provide negative input (erasing) by pressing &quot;A&quot; (toggle label value between 0 and the originally selected label). An example of a user correction is going from <br /> <br /> [[File:KSliceNegativeInput.png|500px]]<br /> <br /> to<br /> <br /> [[File:KSliceCorrectedUI.png|500px]]<br /> <br /> Step 13.) (Used in any order) Certain image regions (depending on the energy chosen and the image itself) may be highly attractive for the evolving contour despite being incorrect according to the user's expert knowledge. He has two options. First, apply repeated input several times (soft user constraints). Second, he can press &quot;B&quot; (toggle between soft/hard constraints) to increase the weight on his inputs, essentially making them hard constraints. Further, constraints can be provided pre-emptively, by erasing/drawing in regions before a contour has reached these areas to prevent leaks in the future or expedite the evolution process, respectively. <br /> <br /> <br /> Step 14.)(Used in any order) Press &quot;F&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the Local Global Chan-Vese. The user can switch the energy he is using at any time.<br /> <br /> Step 15.) (Used in any order) Press &quot;U&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the curvature flow. The user can switch the energy he is using at any time.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> <br /> == Optional Visualization ==<br /> <br /> Step 16.) (Optional) Turn on “Volume Rendering” of the label image in 3D (notice the parameters set for faster interaction). This will be updated during the evolution<br /> <br /> [[File:VolumeRederingOnMeningioma.png|500px]]<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> Step 17.) Examine the current segmentation by flipping through the 2D viewers, providing input as in Step.12 to guide the evolution. <br /> <br /> [[File:ExampleUIDuringProcess.png|500px]] <br /> <br /> <br /> <br /> Step 18.) Repeat steps 10-15 (or any subset of them) until convergence to the desired segmentation.<br /> <br /> [[File:KSliceFinalSegMeningioma.png|500px]] <br /> <br /> <br /> Step 19.) Once satisfied with the segmentation, stop CarreraSlice by clicking &quot;Stop Interactive Segmentor&quot;<br /> <br /> [[File:StopInteractiveSegmenter.png|500px]]<br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> There are two parameters to set: the radius that sets the band around the current initialisation in which image data is considered (for Local-Global Chan-Vese energy) and the number of contour iterations. A large radius means data from far lying regions influences the segmentation (this increases the computational complexity). A small radius ensures that only nearby pixels influence the contour (but set too small will require a better contour initialisation from the user). The number of iterations refers to the time for which the segmenting surface is allowed to evolve before querying the user for input. A small number allows the user to catch the surface before a large error is made, reducing corrective inputs required but may be tedious/slow.<br /> <br /> <br /> <br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-parametersdescription}}--&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> *Kolesov, I., Karasev, P., Muller, G., Chudy, K., Xerogeanes, J., and Tannenbaum, A., “Human supervisory control framework for interactive medical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Biomechanics for Medicine Workshop, Proceeding of the, 2011.<br /> <br /> *Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., and Tannenbaum, A., “Interactive mri segmentation with controlled active vision,” in IEEE Conference on Decision and Control, Proceedings of the, IEEE, Dec. 2011.<br /> <br /> *Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., and Tannenbaum, A.. “Interactive Medical Image Segmentation using PDE Control Of Active Contours”. In: Medical Imaging, IEEE Transactions on (In Submission) (2013)<br /> <br /> *Xerogeanes, J., Mitchell, P. , Karasev, P., Kolesov, I. and Romine, S. . “An Anatomic and Morphologic Evaluation of the Quadriceps Tendon Using 3D MRI Reconstruction: Applications for ACL Autograft Choice and Procurement”. In: Journal of Bone and Joint Surgery (In Submission) (2013)<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/CarreraSliceInteractiveSegmenter&diff=37023 2014-01-16T04:23:21Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: LiangJia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is the CarreraSlice interactive segmentation method. The computation is initialized using an improved GrowCut implementation and refined by an interactive, level set based segmentation algorithm, KSlice. As the automatic segmentation runs and displays the current state, a human user can influence the algorithm’s behavior to more closely align with an expected result.<br /> In the end, the user has created an excellent segmentation result with a minimal amount of time and effort. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finish editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be conducted a couple of times until satisfied<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to work with KSlice for smoothing. The energe functionals available include:<br /> * (a) press 'F' for local-global segmentation<br /> * (b) press 'U' for mean curvature smoothing<br /> * (c) press 'E' for Chan-Vese segmentation<br /> <br /> Step 8.) Select the rectangle tool(or any other painting tool).<br /> <br /> [[File:ImplicitRectangle.png|50px]]<br /> <br /> and initialize CarreraSlice in a 2D slice by drawing inside the structure of interest.<br /> <br /> [[File:KSliceInitMeningioma.png|500px]]<br /> <br /> <br /> Step 9.) Press the “C” key to copy the current initialization in this 2D plane, then press the left/right arrow on the keyboard to move on to an adjacent slice, press the “V” key to paste the label, thereby initializing the current slice. Once this procedure has been repeated several times, we have initialized the surface to a cube in 3D.<br /> <br /> [[File:KSliceInit3DCube.png|500px]]<br /> <br /> <br /> Step 10.) To evolve the 2D contour on a slice, hover the mouse over a 2D view plane, press “Q”.<br /> <br /> [[File:KSlice2DEvolve.png|500px]]<br /> <br /> <br /> <br /> Step 11.) Evolve the contour in 3D by pressing &quot;E&quot; to use the Chan-Vese energy.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> == As necessary, take steps at any time in the process ==<br /> <br /> Step 12.)(Used in any order) A segmenting surface may leak into undesirable areas, based purely on image data. It is the user's job to guide the evolution process. A user can provide positive input (drawing) by selecting a paint tool, painting in areas which should be part of the object of interest but are not. Or, the user can provide negative input (erasing) by pressing &quot;A&quot; (toggle label value between 0 and the originally selected label). An example of a user correction is going from <br /> <br /> [[File:KSliceNegativeInput.png|500px]]<br /> <br /> to<br /> <br /> [[File:KSliceCorrectedUI.png|500px]]<br /> <br /> Step 13.) (Used in any order) Certain image regions (depending on the energy chosen and the image itself) may be highly attractive for the evolving contour despite being incorrect according to the user's expert knowledge. He has two options. First, apply repeated input several times (soft user constraints). Second, he can press &quot;B&quot; (toggle between soft/hard constraints) to increase the weight on his inputs, essentially making them hard constraints. Further, constraints can be provided pre-emptively, by erasing/drawing in regions before a contour has reached these areas to prevent leaks in the future or expedite the evolution process, respectively. <br /> <br /> <br /> Step 14.)(Used in any order) Press &quot;F&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the Local Global Chan-Vese. The user can switch the energy he is using at any time.<br /> <br /> Step 15.) (Used in any order) Press &quot;U&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the curvature flow. The user can switch the energy he is using at any time.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> <br /> == Optional Visualization ==<br /> <br /> Step 16.) (Optional) Turn on “Volume Rendering” of the label image in 3D (notice the parameters set for faster interaction). This will be updated during the evolution<br /> <br /> [[File:VolumeRederingOnMeningioma.png|500px]]<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> Step 17.) Examine the current segmentation by flipping through the 2D viewers, providing input as in Step.12 to guide the evolution. <br /> <br /> [[File:ExampleUIDuringProcess.png|500px]] <br /> <br /> <br /> <br /> Step 18.) Repeat steps 10-15 (or any subset of them) until convergence to the desired segmentation.<br /> <br /> [[File:KSliceFinalSegMeningioma.png|500px]] <br /> <br /> <br /> Step 19.) Once satisfied with the segmentation, stop CarreraSlice by clicking &quot;Stop Interactive Segmentor&quot;<br /> <br /> [[File:StopInteractiveSegmenter.png|500px]]<br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> There are two parameters to set: the radius that sets the band around the current initialisation in which image data is considered (for Local-Global Chan-Vese energy) and the number of contour iterations. A large radius means data from far lying regions influences the segmentation (this increases the computational complexity). A small radius ensures that only nearby pixels influence the contour (but set too small will require a better contour initialisation from the user). The number of iterations refers to the time for which the segmenting surface is allowed to evolve before querying the user for input. A small number allows the user to catch the surface before a large error is made, reducing corrective inputs required but may be tedious/slow.<br /> <br /> <br /> <br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-parametersdescription}}--&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> *Kolesov, I., Karasev, P., Muller, G., Chudy, K., Xerogeanes, J., and Tannenbaum, A., “Human supervisory control framework for interactive medical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Biomechanics for Medicine Workshop, Proceeding of the, 2011.<br /> <br /> *Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., and Tannenbaum, A., “Interactive mri segmentation with controlled active vision,” in IEEE Conference on Decision and Control, Proceedings of the, IEEE, Dec. 2011.<br /> <br /> *Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., and Tannenbaum, A.. “Interactive Medical Image Segmentation using PDE Control Of Active Contours”. In: Medical Imaging, IEEE Transactions on (In Submission) (2013)<br /> <br /> *Xerogeanes, J., Mitchell, P. , Karasev, P., Kolesov, I. and Romine, S. . “An Anatomic and Morphologic Evaluation of the Quadriceps Tendon Using 3D MRI Reconstruction: Applications for ACL Autograft Choice and Procurement”. In: Journal of Bone and Joint Surgery (In Submission) (2013)<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/CarreraSliceInteractiveSegmenter&diff=37022 2014-01-16T04:20:35Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: LiangJia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is the CarreraSlice interactive segmentation method. The computation is initialized using an improved GrowCut implementation and refined by an interactive, level set based segmentation algorithm, KSlice. As the automatic segmentation runs and displays the current state, a human user can influence the algorithm’s behavior to more closely align with an expected result.<br /> In the end, the user has created an excellent segmentation result with a minimal amount of time and effort. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> [[File:FGCSeed2.png|500px]]<br /> <br /> * (c) Once finish editing on the seed image, press 'G' to run fast GrowCut again. <br /> [[File:FGCSeg2.png|500px]]<br /> <br /> The steps 6 (b) and (c) may be conducted a couple of times until satisfied<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to work with KSlice for smoothing. The level-sets functionals available include:<br /> * (a) press 'F' to do local-global smoothing<br /> * (b) press 'U' to do mean curvature smoothing<br /> <br /> Step 8.) Select the rectangle tool(or any other painting tool).<br /> <br /> [[File:ImplicitRectangle.png|50px]]<br /> <br /> and initialize CarreraSlice in a 2D slice by drawing inside the structure of interest.<br /> <br /> [[File:KSliceInitMeningioma.png|500px]]<br /> <br /> <br /> Step 9.) Press the “C” key to copy the current initialization in this 2D plane, then press the left/right arrow on the keyboard to move on to an adjacent slice, press the “V” key to paste the label, thereby initializing the current slice. Once this procedure has been repeated several times, we have initialized the surface to a cube in 3D.<br /> <br /> [[File:KSliceInit3DCube.png|500px]]<br /> <br /> <br /> Step 10.) To evolve the 2D contour on a slice, hover the mouse over a 2D view plane, press “Q”.<br /> <br /> [[File:KSlice2DEvolve.png|500px]]<br /> <br /> <br /> <br /> Step 11.) Evolve the contour in 3D by pressing &quot;E&quot; to use the Chan-Vese energy.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> == As necessary, take steps at any time in the process ==<br /> <br /> Step 12.)(Used in any order) A segmenting surface may leak into undesirable areas, based purely on image data. It is the user's job to guide the evolution process. A user can provide positive input (drawing) by selecting a paint tool, painting in areas which should be part of the object of interest but are not. Or, the user can provide negative input (erasing) by pressing &quot;A&quot; (toggle label value between 0 and the originally selected label). An example of a user correction is going from <br /> <br /> [[File:KSliceNegativeInput.png|500px]]<br /> <br /> to<br /> <br /> [[File:KSliceCorrectedUI.png|500px]]<br /> <br /> Step 13.) (Used in any order) Certain image regions (depending on the energy chosen and the image itself) may be highly attractive for the evolving contour despite being incorrect according to the user's expert knowledge. He has two options. First, apply repeated input several times (soft user constraints). Second, he can press &quot;B&quot; (toggle between soft/hard constraints) to increase the weight on his inputs, essentially making them hard constraints. Further, constraints can be provided pre-emptively, by erasing/drawing in regions before a contour has reached these areas to prevent leaks in the future or expedite the evolution process, respectively. <br /> <br /> <br /> Step 14.)(Used in any order) Press &quot;F&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the Local Global Chan-Vese. The user can switch the energy he is using at any time.<br /> <br /> Step 15.) (Used in any order) Press &quot;U&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the curvature flow. The user can switch the energy he is using at any time.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> <br /> == Optional Visualization ==<br /> <br /> Step 16.) (Optional) Turn on “Volume Rendering” of the label image in 3D (notice the parameters set for faster interaction). This will be updated during the evolution<br /> <br /> [[File:VolumeRederingOnMeningioma.png|500px]]<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> Step 17.) Examine the current segmentation by flipping through the 2D viewers, providing input as in Step.12 to guide the evolution. <br /> <br /> [[File:ExampleUIDuringProcess.png|500px]] <br /> <br /> <br /> <br /> Step 18.) Repeat steps 10-15 (or any subset of them) until convergence to the desired segmentation.<br /> <br /> [[File:KSliceFinalSegMeningioma.png|500px]] <br /> <br /> <br /> Step 19.) Once satisfied with the segmentation, stop CarreraSlice by clicking &quot;Stop Interactive Segmentor&quot;<br /> <br /> [[File:StopInteractiveSegmenter.png|500px]]<br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> There are two parameters to set: the radius that sets the band around the current initialisation in which image data is considered (for Local-Global Chan-Vese energy) and the number of contour iterations. A large radius means data from far lying regions influences the segmentation (this increases the computational complexity). A small radius ensures that only nearby pixels influence the contour (but set too small will require a better contour initialisation from the user). The number of iterations refers to the time for which the segmenting surface is allowed to evolve before querying the user for input. A small number allows the user to catch the surface before a large error is made, reducing corrective inputs required but may be tedious/slow.<br /> <br /> <br /> <br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-parametersdescription}}--&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> *Kolesov, I., Karasev, P., Muller, G., Chudy, K., Xerogeanes, J., and Tannenbaum, A., “Human supervisory control framework for interactive medical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Biomechanics for Medicine Workshop, Proceeding of the, 2011.<br /> <br /> *Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., and Tannenbaum, A., “Interactive mri segmentation with controlled active vision,” in IEEE Conference on Decision and Control, Proceedings of the, IEEE, Dec. 2011.<br /> <br /> *Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., and Tannenbaum, A.. “Interactive Medical Image Segmentation using PDE Control Of Active Contours”. In: Medical Imaging, IEEE Transactions on (In Submission) (2013)<br /> <br /> *Xerogeanes, J., Mitchell, P. , Karasev, P., Kolesov, I. and Romine, S. . “An Anatomic and Morphologic Evaluation of the Quadriceps Tendon Using 3D MRI Reconstruction: Applications for ACL Autograft Choice and Procurement”. In: Journal of Bone and Joint Surgery (In Submission) (2013)<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGCSeg2.png&diff=37021 2014-01-16T04:15:21Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGCSeed2.png&diff=37020 2014-01-16T04:15:07Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGCSeg1.png&diff=37019 2014-01-16T04:08:40Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:FGCSeed.png&diff=37018 2014-01-16T04:08:19Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/CarreraSliceInteractiveSegmenter&diff=37017 2014-01-16T04:07:42Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Author: Ivan Kolesov, Stony Brook University&lt;br&gt;<br /> Author: LiangJia Zhu, Stony Brook University&lt;br&gt;<br /> Author: Yi Gao, University of Alabama Birmingham&lt;br&gt;<br /> Author: Peter Karasev, Georgia Institute of Technology&lt;br&gt;<br /> Author: Patricio Vela, Georgia Institute of Technology&lt;br&gt;<br /> Author: Allen Tannenbaum, Stony Brook University&lt;br&gt;<br /> Contributor1: Steve Pieper, Isomics, Inc.&lt;br&gt;<br /> Contributor2: Karl Fritscher (BWH &amp; MGH)&lt;br&gt;<br /> &lt;!--Contributor2: FIRSTNAME LASTNAME, AFFILIATION&lt;br&gt; --&gt;<br /> &lt;!--Contact: FIRSTNAME LASTNAME, &lt;email&gt;john@doe.org&lt;/email&gt;&lt;br&gt; --&gt;<br /> <br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |[[File:GeorgiaTech_Logo.svg]]<br /> |{{collaborator|logo|spl}}|{{collaborator|longname|spl}} &lt;-Replace this logo with yours<br /> }} --&gt;<br /> <br /> <br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> <br /> This is the CarreraSlice interactive segmentation method. The computation is initialized using an improved GrowCut implementation and refined by an interactive, level set based segmentation algorithm, KSlice. As the automatic segmentation runs and displays the current state, a human user can influence the algorithm’s behavior to more closely align with an expected result.<br /> In the end, the user has created an excellent segmentation result with a minimal amount of time and effort. Please see the references below for more details.<br /> <br /> &lt;!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> {{documentation/{{documentation/version}}/module-description}} --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> <br /> <br /> Step 1.) Add data volume to segment<br /> <br /> [[File:LoadMeningioma.png|500px]]<br /> <br /> <br /> Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu<br /> <br /> [[File:StartEditorMeningioma.png|500px]]<br /> <br /> <br /> Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu<br /> <br /> [[File:CarreraSliceEffect.png|50px]]<br /> <br /> <br /> Step 4.) Set the “Radius” parameter, the &quot;Number of Iterations&quot; and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)<br /> <br /> [[File:StartBotMeningioma.png|500px]]<br /> <br /> <br /> Step 5.) Turn “On” all three slice views in the 3D Plane<br /> <br /> [[File:TurnOnSlices3DMeningioma.png|500px]]<br /> <br /> Step 6.) Initialize the segmentation using fast GrowCut<br /> * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.<br /> <br /> * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions. <br /> <br /> * (c) Once finish editing on the seed image, press 'G' to run fast GrowCut again. <br /> <br /> The steps 6 (b) and (c) may be conducted a couple of times until satisfied<br /> <br /> Step 7.) Once satisfied with the initialization, press 'M' to work with KSlice.<br /> <br /> Step 8.) Select the rectangle tool(or any other painting tool).<br /> <br /> [[File:ImplicitRectangle.png|50px]]<br /> <br /> and initialize CarreraSlice in a 2D slice by drawing inside the structure of interest.<br /> <br /> [[File:KSliceInitMeningioma.png|500px]]<br /> <br /> <br /> Step 9.) Press the “C” key to copy the current initialization in this 2D plane, then press the left/right arrow on the keyboard to move on to an adjacent slice, press the “V” key to paste the label, thereby initializing the current slice. Once this procedure has been repeated several times, we have initialized the surface to a cube in 3D.<br /> <br /> [[File:KSliceInit3DCube.png|500px]]<br /> <br /> <br /> Step 10.) To evolve the 2D contour on a slice, hover the mouse over a 2D view plane, press “Q”.<br /> <br /> [[File:KSlice2DEvolve.png|500px]]<br /> <br /> <br /> <br /> Step 11.) Evolve the contour in 3D by pressing &quot;E&quot; to use the Chan-Vese energy.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> == As necessary, take steps at any time in the process ==<br /> <br /> Step 12.)(Used in any order) A segmenting surface may leak into undesirable areas, based purely on image data. It is the user's job to guide the evolution process. A user can provide positive input (drawing) by selecting a paint tool, painting in areas which should be part of the object of interest but are not. Or, the user can provide negative input (erasing) by pressing &quot;A&quot; (toggle label value between 0 and the originally selected label). An example of a user correction is going from <br /> <br /> [[File:KSliceNegativeInput.png|500px]]<br /> <br /> to<br /> <br /> [[File:KSliceCorrectedUI.png|500px]]<br /> <br /> Step 13.) (Used in any order) Certain image regions (depending on the energy chosen and the image itself) may be highly attractive for the evolving contour despite being incorrect according to the user's expert knowledge. He has two options. First, apply repeated input several times (soft user constraints). Second, he can press &quot;B&quot; (toggle between soft/hard constraints) to increase the weight on his inputs, essentially making them hard constraints. Further, constraints can be provided pre-emptively, by erasing/drawing in regions before a contour has reached these areas to prevent leaks in the future or expedite the evolution process, respectively. <br /> <br /> <br /> Step 14.)(Used in any order) Press &quot;F&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the Local Global Chan-Vese. The user can switch the energy he is using at any time.<br /> <br /> Step 15.) (Used in any order) Press &quot;U&quot; to run the contour in 3D, similarly to Step.11 but using an alternative energy, the curvature flow. The user can switch the energy he is using at any time.<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> <br /> == Optional Visualization ==<br /> <br /> Step 16.) (Optional) Turn on “Volume Rendering” of the label image in 3D (notice the parameters set for faster interaction). This will be updated during the evolution<br /> <br /> [[File:VolumeRederingOnMeningioma.png|500px]]<br /> <br /> <br /> ---- &lt;!-- Horizontal rule --&gt;<br /> <br /> Step 17.) Examine the current segmentation by flipping through the 2D viewers, providing input as in Step.12 to guide the evolution. <br /> <br /> [[File:ExampleUIDuringProcess.png|500px]] <br /> <br /> <br /> <br /> Step 18.) Repeat steps 10-15 (or any subset of them) until convergence to the desired segmentation.<br /> <br /> [[File:KSliceFinalSegMeningioma.png|500px]] <br /> <br /> <br /> Step 19.) Once satisfied with the segmentation, stop CarreraSlice by clicking &quot;Stop Interactive Segmentor&quot;<br /> <br /> [[File:StopInteractiveSegmenter.png|500px]]<br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> There are two parameters to set: the radius that sets the band around the current initialisation in which image data is considered (for Local-Global Chan-Vese energy) and the number of contour iterations. A large radius means data from far lying regions influences the segmentation (this increases the computational complexity). A small radius ensures that only nearby pixels influence the contour (but set too small will require a better contour initialisation from the user). The number of iterations refers to the time for which the segmenting surface is allowed to evolve before querying the user for input. A small number allows the user to catch the surface before a large error is made, reducing corrective inputs required but may be tedious/slow.<br /> <br /> <br /> <br /> <br /> &lt;!--{{documentation/{{documentation/version}}/module-parametersdescription}}--&gt;<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> &lt;!-- {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> N/A --&gt;<br /> <br /> <br /> <br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> *Kolesov, I., Karasev, P., Muller, G., Chudy, K., Xerogeanes, J., and Tannenbaum, A., “Human supervisory control framework for interactive medical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Biomechanics for Medicine Workshop, Proceeding of the, 2011.<br /> <br /> *Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., and Tannenbaum, A., “Interactive mri segmentation with controlled active vision,” in IEEE Conference on Decision and Control, Proceedings of the, IEEE, Dec. 2011.<br /> <br /> *Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., and Tannenbaum, A.. “Interactive Medical Image Segmentation using PDE Control Of Active Contours”. In: Medical Imaging, IEEE Transactions on (In Submission) (2013)<br /> <br /> *Xerogeanes, J., Mitchell, P. , Karasev, P., Kolesov, I. and Romine, S. . “An Anatomic and Morphologic Evaluation of the Quadriceps Tendon Using 3D MRI Reconstruction: Applications for ACL Autograft Choice and Procurement”. In: Journal of Bone and Joint Surgery (In Submission) (2013)<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Developer_Meetings/20131008&diff=36476 2013-11-09T18:38:36Z

<p>Liangjiazhu: /* Recently updated Module Documentations */</p> <hr /> <div>__TOC__<br /> <br /> == Update documentation ==<br /> * Ron's priorities for final year of NAMIC funding<br /> ===Not yet done===<br /> # Update the slicer user documentation pages by RSNA. Describe up-to-date workflows in the examples. Make sure all the features are described. Make sure authors, institutions, and grants are properly acknowledged. The following is an incomplete list:<br /> ## Probe Volume With Model (Lauren O'Donnell) <br /> ## ACPC Transform (Nicole Aucoin)<br /> ## Cast Scalar Volume (Nicole Aucoin)<br /> ## Colors (Nicole Aucoin)<br /> ## LUTs (Nicole Aucoin)<br /> ## Mask Scalar Volume (Nicole Aucoin)<br /> ## Merge Models (Nicole Aucoin)<br /> ## Model Maker (Nicole Aucoin)<br /> ## Models (Nicole Aucoin)<br /> ## Model To LabelMap (Nicole Aucoin)<br /> ## PET Standard Uptake Value Computation (Nicole Aucoin)<br /> ## Threshold Scalar Volume (Nicole Aucoin)<br /> ## Fiducial Registration (Stephen Aylward)<br /> ## Gaussian Blur Image Filter (Stephen Aylward)<br /> ## Resample Scalar/Vector/DWI Volume (Francois Budin)<br /> ## Data (Julien Finet)<br /> ## Models (Julien Finet)<br /> ## Robust Statistics Segmenter (Yi Gao)<br /> ## Segmentation Aided Registration (Yi Gao)<br /> ## Reformat (Michael Jeulin-Lagarrigue)<br /> ## Demon Registration (BRAINS) (Hans Johnson)<br /> ## Foreground Masking (BRAINS) (Hans Johnson)<br /> ## General Brainsfit Registration (Hans Johnson)<br /> ## Resample Image (BRAINS) (Hans Johnson)<br /> ## Vector Demon Registration (Hans Johnson)<br /> ## KSlice Interactive Segmenter (Ivan Kolesov)<br /> ## Simple Filters (Bradley Lowekamp)<br /> ## WatershedFromMarkers (Bradley Lowekamp)<br /> ## SNR Measurement (Babak Matinfar)<br /> ## Main window (JC) <br /> ## Welcome to Slicer (JC) <br /> ## Add Scalar Volumes (Jim Miller)<br /> ## CheckerBoard Filter (Jim Miller)<br /> ## Create DICOM Series (Jim Miller)<br /> ## Curvature Anisotropic Diffusion (Jim Miller)<br /> ## Dicom to Nrrd Converter (Jim Miller)<br /> ## Extract Skeleton (Jim Miller)<br /> ## Gradient Anisotropic Diffusion (Jim Miller)<br /> ## Grayscale Fill Hole Image Filter (Jim Miller)<br /> ## Grayscale Grind Peak Image Filter (Jim Miller)<br /> ## Grayscale Model Maker (Jim Miller)<br /> ## Histogram Matching (Jim Miller)<br /> ## Median Image Filter (Jim Miller)<br /> ## Multiply Scalar Volumes (Jim Miller)<br /> ## Orient Scalar Volume (Jim Miller)<br /> ## Simple Region Growing Segmentation (Jim Miller)<br /> ## Subtract Scalar Volumes (Jim Miller)<br /> ## View Controllers (Jim Miller)<br /> ## Voting Binary Hole Filling Image Filter (Jim Miller)<br /> ## Label Map Smoothing (Dirk Padfield)<br /> ## Data Probe (Steve Pieper)<br /> ## DICOM (Steve Pieper)<br /> ## Editor (Steve Pieper)<br /> ## Endoscopy (Steve Pieper)<br /> ## Fiber Bundle to Label Map (Steve Pieper)<br /> ## Label Statistics (Steve Pieper)<br /> ## .mrb (Steve Pieper)<br /> ## Sample Data (Steve Pieper)<br /> ## Vector to Scalar Volume (Steve Pieper)<br /> ## Volumes (Steve Pieper)<br /> ## EMSegment Command-line (Kilian Pohl)<br /> ## EMSegment Easy (no atlas) (Kilian Pohl)<br /> ## EMSegment (with atlas) (Kilian Pohl)<br /> ## DTIExport (Sonia Pujol)<br /> ## DTIImport (Sonia Pujol)<br /> ## OpenIGTLink IF (Junichi Tokuda)<br /> ## Diffusion Tensor Scalar Measurements (Demian Wasserman)<br /> ## Diffusion Weighted Volume Masking (Demian Wasserman)<br /> ## DWI Joint Rician LMMSE Image Filter (Demian Wasserman)<br /> ## DWI Rician LMMSE Filter (Demian Wasserman)<br /> ## DWI To DTI Estimation (Demian Wasserman)<br /> ## DWI to Full Brain Tractography (Demian Wasserman)<br /> ## Resample DTI Volume (Demian Wasserman)<br /> ## Tractography Label Map Seeding (Demian Wasserman)<br /> ## Image Label Combine (Alex Yarmarkovich)<br /> ## Sceneviews (Alex Yarmarkovich)<br /> ## Tractography Interactive Seeding (Alex Yarmarkovich)<br /> ## Transforms (Alex Yarmarkovich)<br /> <br /> ===Recently updated Module Documentations===<br /> ## Annotations (Nicole Aucoin) 12:11, 24 September 2013.<br /> ## Markups (Nicole Aucoin) 11:59, 24 September 2013.<br /> ## Volume rendering (including label data sets) (Julien Finet) 11:43, 19 September 2013.<br /> ## Introduction to Registration (Dominik Meier) 16:18, 13 September 2013.<br /> ## Tractography Display (Demian Wasserman) 12:26, 13 September 2013. by Alex Yarmarkovich<br /> ## FiberBundleLabelSelect (Alex Yarmarkovich) 11:33, 13 September 2013.<br /> ## N4ITK Bias Field Correction (Andrey Fedorov) (svn tags updated in 4.3 and Nightly, don't recall how updates are propagated to the wiki page content) 7 Nov 2013<br /> ## Intensity Difference Metric (Andrey Fedorov) -- Not applicable -- this is an extension, not Slicer core 7 Nov 2013<br /> ## Crop Volume (Andrey Fedorov) updated 7 Nov 2013<br /> ## SurfaceToolbox (Luca Antiga) updated 7 Nov 2013<br /> ## Left Atrial Scar Segmenter (LiangJia Zhu) updated 7 Nov 2013<br /> ## Left Atrium Segmenter (LiangJia Zhu) updated 7 Nov 2013<br /> <br /> ==Other improvements==<br /> ## when I save an MRB repeatedly, (1) &lt;del&gt;my customizations from last time should be remembered. Right now, the save module forgets that I selected .mrb for my last save. I need to tell it again.&lt;/del&gt; (2) When doing so, the default name turns into .mrb.mrb. (3) &lt;del&gt;It also forgets that I pointed it to a particular directory and offers to save in the default location.&lt;/del&gt; (4) &lt;del&gt;Having this persist for the session only would be good. If persistent across session, a reset button would be good.&lt;/del&gt; See [http://www.na-mic.org/Bug/view.php?id=3484 #3484] and [http://www.na-mic.org/Bug/view.php?id=2948 #2948]<br /> ##the location from which something was loaded should be the default for saving.<br /> ##create a preset w/l+LUT in the volume module (scaled from 0 to 1), and corresponding transfer function for FA maps in the volume rendering module (bug #1857)<br /> ##on/off switch in the editor module for volume rendering of active label map<br /> # All tutorials should be updated, including new basic features such as .drag and drop and .mrb<br /> # [http://www.na-mic.org/Bug/view.php?id=3452 Unable to use Volume module Threshold widget in a meaningful way ]<br /> #[http://www.na-mic.org/Bug/view.php?id=1299 Better presentation of text in sceneviews]<br /> <br /> #Review critical bugs / Open questions<br /> * Nonlinear transform support (Alex)<br /> ** Resampled on the fly<br /> ** Pre-cached non-linear hardened version<br /> ** Need to fix read/write of grid transforms<br /> ** Fix resample CLIs to consistently accept grid transforms <br /> ** See: http://www.na-mic.org/Wiki/index.php/Handling_deformable_transforms_in_Slicer_meeting_minutes<br /> * Migrating Rulers into Markups module (Nicole)<br /> * Pluggable registration module (Steve, for Head and Neck Radiotherapy DBP)<br /> * Each DBP to be pinged for wish list by Engineering contact<br /> ** Steve -&gt; Greg<br /> ** Jim -&gt; Rob/Josh<br /> ** Stephen -&gt; Jack<br /> ** Dan -&gt; Hans<br /> * Steve will send blanket email to all DBPs requesting their priorities<br /> * In coming months we will finalize a strategy for 4.x releases as final NA-MIC deliverables</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Developer_Meetings/20131008&diff=36475 2013-11-09T18:38:19Z

<p>Liangjiazhu: /* Not yet done */</p> <hr /> <div>__TOC__<br /> <br /> == Update documentation ==<br /> * Ron's priorities for final year of NAMIC funding<br /> ===Not yet done===<br /> # Update the slicer user documentation pages by RSNA. Describe up-to-date workflows in the examples. Make sure all the features are described. Make sure authors, institutions, and grants are properly acknowledged. The following is an incomplete list:<br /> ## Probe Volume With Model (Lauren O'Donnell) <br /> ## ACPC Transform (Nicole Aucoin)<br /> ## Cast Scalar Volume (Nicole Aucoin)<br /> ## Colors (Nicole Aucoin)<br /> ## LUTs (Nicole Aucoin)<br /> ## Mask Scalar Volume (Nicole Aucoin)<br /> ## Merge Models (Nicole Aucoin)<br /> ## Model Maker (Nicole Aucoin)<br /> ## Models (Nicole Aucoin)<br /> ## Model To LabelMap (Nicole Aucoin)<br /> ## PET Standard Uptake Value Computation (Nicole Aucoin)<br /> ## Threshold Scalar Volume (Nicole Aucoin)<br /> ## Fiducial Registration (Stephen Aylward)<br /> ## Gaussian Blur Image Filter (Stephen Aylward)<br /> ## Resample Scalar/Vector/DWI Volume (Francois Budin)<br /> ## Data (Julien Finet)<br /> ## Models (Julien Finet)<br /> ## Robust Statistics Segmenter (Yi Gao)<br /> ## Segmentation Aided Registration (Yi Gao)<br /> ## Reformat (Michael Jeulin-Lagarrigue)<br /> ## Demon Registration (BRAINS) (Hans Johnson)<br /> ## Foreground Masking (BRAINS) (Hans Johnson)<br /> ## General Brainsfit Registration (Hans Johnson)<br /> ## Resample Image (BRAINS) (Hans Johnson)<br /> ## Vector Demon Registration (Hans Johnson)<br /> ## KSlice Interactive Segmenter (Ivan Kolesov)<br /> ## Simple Filters (Bradley Lowekamp)<br /> ## WatershedFromMarkers (Bradley Lowekamp)<br /> ## SNR Measurement (Babak Matinfar)<br /> ## Main window (JC) <br /> ## Welcome to Slicer (JC) <br /> ## Add Scalar Volumes (Jim Miller)<br /> ## CheckerBoard Filter (Jim Miller)<br /> ## Create DICOM Series (Jim Miller)<br /> ## Curvature Anisotropic Diffusion (Jim Miller)<br /> ## Dicom to Nrrd Converter (Jim Miller)<br /> ## Extract Skeleton (Jim Miller)<br /> ## Gradient Anisotropic Diffusion (Jim Miller)<br /> ## Grayscale Fill Hole Image Filter (Jim Miller)<br /> ## Grayscale Grind Peak Image Filter (Jim Miller)<br /> ## Grayscale Model Maker (Jim Miller)<br /> ## Histogram Matching (Jim Miller)<br /> ## Median Image Filter (Jim Miller)<br /> ## Multiply Scalar Volumes (Jim Miller)<br /> ## Orient Scalar Volume (Jim Miller)<br /> ## Simple Region Growing Segmentation (Jim Miller)<br /> ## Subtract Scalar Volumes (Jim Miller)<br /> ## View Controllers (Jim Miller)<br /> ## Voting Binary Hole Filling Image Filter (Jim Miller)<br /> ## Label Map Smoothing (Dirk Padfield)<br /> ## Data Probe (Steve Pieper)<br /> ## DICOM (Steve Pieper)<br /> ## Editor (Steve Pieper)<br /> ## Endoscopy (Steve Pieper)<br /> ## Fiber Bundle to Label Map (Steve Pieper)<br /> ## Label Statistics (Steve Pieper)<br /> ## .mrb (Steve Pieper)<br /> ## Sample Data (Steve Pieper)<br /> ## Vector to Scalar Volume (Steve Pieper)<br /> ## Volumes (Steve Pieper)<br /> ## EMSegment Command-line (Kilian Pohl)<br /> ## EMSegment Easy (no atlas) (Kilian Pohl)<br /> ## EMSegment (with atlas) (Kilian Pohl)<br /> ## DTIExport (Sonia Pujol)<br /> ## DTIImport (Sonia Pujol)<br /> ## OpenIGTLink IF (Junichi Tokuda)<br /> ## Diffusion Tensor Scalar Measurements (Demian Wasserman)<br /> ## Diffusion Weighted Volume Masking (Demian Wasserman)<br /> ## DWI Joint Rician LMMSE Image Filter (Demian Wasserman)<br /> ## DWI Rician LMMSE Filter (Demian Wasserman)<br /> ## DWI To DTI Estimation (Demian Wasserman)<br /> ## DWI to Full Brain Tractography (Demian Wasserman)<br /> ## Resample DTI Volume (Demian Wasserman)<br /> ## Tractography Label Map Seeding (Demian Wasserman)<br /> ## Image Label Combine (Alex Yarmarkovich)<br /> ## Sceneviews (Alex Yarmarkovich)<br /> ## Tractography Interactive Seeding (Alex Yarmarkovich)<br /> ## Transforms (Alex Yarmarkovich)<br /> <br /> ===Recently updated Module Documentations===<br /> ## Annotations (Nicole Aucoin) 12:11, 24 September 2013.<br /> ## Markups (Nicole Aucoin) 11:59, 24 September 2013.<br /> ## Volume rendering (including label data sets) (Julien Finet) 11:43, 19 September 2013.<br /> ## Introduction to Registration (Dominik Meier) 16:18, 13 September 2013.<br /> ## Tractography Display (Demian Wasserman) 12:26, 13 September 2013. by Alex Yarmarkovich<br /> ## FiberBundleLabelSelect (Alex Yarmarkovich) 11:33, 13 September 2013.<br /> ## N4ITK Bias Field Correction (Andrey Fedorov) (svn tags updated in 4.3 and Nightly, don't recall how updates are propagated to the wiki page content) 7 Nov 2013<br /> ## Intensity Difference Metric (Andrey Fedorov) -- Not applicable -- this is an extension, not Slicer core 7 Nov 2013<br /> ## Crop Volume (Andrey Fedorov) updated 7 Nov 2013<br /> ## SurfaceToolbox (Luca Antiga) updated 7 Nov 2013<br /> <br /> ==Other improvements==<br /> ## when I save an MRB repeatedly, (1) &lt;del&gt;my customizations from last time should be remembered. Right now, the save module forgets that I selected .mrb for my last save. I need to tell it again.&lt;/del&gt; (2) When doing so, the default name turns into .mrb.mrb. (3) &lt;del&gt;It also forgets that I pointed it to a particular directory and offers to save in the default location.&lt;/del&gt; (4) &lt;del&gt;Having this persist for the session only would be good. If persistent across session, a reset button would be good.&lt;/del&gt; See [http://www.na-mic.org/Bug/view.php?id=3484 #3484] and [http://www.na-mic.org/Bug/view.php?id=2948 #2948]<br /> ##the location from which something was loaded should be the default for saving.<br /> ##create a preset w/l+LUT in the volume module (scaled from 0 to 1), and corresponding transfer function for FA maps in the volume rendering module (bug #1857)<br /> ##on/off switch in the editor module for volume rendering of active label map<br /> # All tutorials should be updated, including new basic features such as .drag and drop and .mrb<br /> # [http://www.na-mic.org/Bug/view.php?id=3452 Unable to use Volume module Threshold widget in a meaningful way ]<br /> #[http://www.na-mic.org/Bug/view.php?id=1299 Better presentation of text in sceneviews]<br /> <br /> #Review critical bugs / Open questions<br /> * Nonlinear transform support (Alex)<br /> ** Resampled on the fly<br /> ** Pre-cached non-linear hardened version<br /> ** Need to fix read/write of grid transforms<br /> ** Fix resample CLIs to consistently accept grid transforms <br /> ** See: http://www.na-mic.org/Wiki/index.php/Handling_deformable_transforms_in_Slicer_meeting_minutes<br /> * Migrating Rulers into Markups module (Nicole)<br /> * Pluggable registration module (Steve, for Head and Neck Radiotherapy DBP)<br /> * Each DBP to be pinged for wish list by Engineering contact<br /> ** Steve -&gt; Greg<br /> ** Jim -&gt; Rob/Josh<br /> ** Stephen -&gt; Jack<br /> ** Dan -&gt; Hans<br /> * Steve will send blanket email to all DBPs requesting their priorities<br /> * In coming months we will finalize a strategy for 4.x releases as final NA-MIC deliverables</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/4.3/Modules&diff=36340 2013-11-01T05:26:26Z

<p>Liangjiazhu: /* Segmentation */</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> =Modules by Category=<br /> ==Core Modules==<br /> <br /> {{documentation/{{documentation/version}}/Developers/random-image}}<br /> <br /> *[[Documentation/{{documentation/version}}/Modules/Annotations|Annotations]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Colors|Colors]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Data|Data]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/DICOM|DICOM]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> **[[Documentation/{{documentation/version}}/Modules/Editor/WatershedFromMarkers|WatershedFromMarkers]] (Bradley Lowekamp) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Markups|Markups]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/Models|Models]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/SceneViews|SceneViews]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Transforms|Transforms]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ViewControllers|View Controllers]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/Volumes|Volumes]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/VolumeRendering|Volume Rendering]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/SlicerWelcome|Welcome to Slicer]] (Wendy Plesniak)<br /> <br /> ==Wizards==<br /> <br /> ==Informatics==<br /> *[[Documentation/{{documentation/version}}/Modules/SampleData|Sample Data]] (Steve Pieper)<br /> <br /> ==Registration==<br /> *[[:Category:Documentation/{{documentation/version}}/Modules/Registration|Introduction to Registration]] (Dominik Meier)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSFit|General Brainsfit Registration]] (Hans Johnson)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample Image (BRAINS)]] (Hans Johnson)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/ACPCTransform|ACPC Transform]] (Nicole Aucoin)<br /> **[[Documentation/{{documentation/version}}/Modules/BRAINSDemonWarp|Demon Registration (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/FiducialRegistration|Fiducial Registration]] (Stephen Aylward) {{wip}}<br /> **Vector Demon Registration (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/Reformat|Reformat]] (Michael Jeulin-Lagarrigue)<br /> **[[Documentation/{{documentation/version}}/Modules/SegmentationAidedRegistration|Segmentation Aided Registration]] (Yi Gao) {{wip}}<br /> <br /> ==Segmentation==<br /> *[[Documentation/{{documentation/version}}/Modules/EMSegment Easy|EMSegment Easy (no atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/EMSegmenter|EMSegment (with atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]] (Jim Miller, Harini Veeraraghavan) <br /> *[[Documentation/{{documentation/version}}/Modules/KSliceInteractiveSegmenter|KSlice Interactive Segmenter]] (Ivan Kolesov, Peter Karasev, Patricio Vela, Allen Tannenbaum, Karl Fritscher, Steve Pieper )<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/EMSegment Command-line | EMSegment Command-line]] (Kilian Pohl)<br /> **[[Documentation/{{documentation/version}}/Modules/ForegroundMasking|Foreground Masking (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/RobustStatisticsSegmenter|Robust Statistics Segmenter]] (Yi Gao)<br /> **[[Documentation/{{documentation/version}}/Modules/LAScarSegmenter|Left Atrial Scar Segmenter]] (Liangjia Zhu) <br /> **[[Documentation/{{documentation/version}}/Modules/LASegmenter|Left Atrium Segmenter]] (Liangjia Zhu)<br /> <br /> ==Quantification==<br /> *[[Documentation/{{documentation/version}}/Modules/DataProbe|Data Probe]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/LabelStatistics|Label Statistics]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/PETStandardUptakeValueComputation|PET Standard Uptake Value Computation]] (Nicole Aucoin)<br /> * Change quantification<br /> ** [[Documentation/{{documentation/version}}/Modules/IntensityDifferenceMetric|Intensity Difference Metric]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/SNR Measurement|SNR Measurement]] (Babak Matinfar)<br /> <br /> ==Diffusion== <br /> *DWI to Full Brain Tractography (Demian Wasserman) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/DWIJointRicianLMMSEFilter|DWI Joint Rician LMMSE Image Filter]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DWIRicianLMMSEFilter|DWI Rician LMMSE Filter]] (Demian Wasserman) {{wip}}<br /> *Tractography<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyDisplay|Tractography Display]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyFiducialSeeding|Tractography Interactive Seeding]] (Alex Yarmarkovich)<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyLabelMapSeeding|Tractography Label Map Seeding]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/UKFTractography|UKF Tractography]]<br /> *Utilities<br /> **[[Documentation/{{documentation/version}}/Modules/DWIToDTIEstimation|DWI To DTI Estimation]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]] (Demian Wasserman) <br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionWeightedVolumeMasking|Diffusion Weighted Volume Masking]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleLabelSelect|FiberBundleLabelSelect]] (Alex Yarmarkovich) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleDTIVolume|Resample DTI Volume]] (Demian Wasserman) {{wip}}<br /> *Data Conversion<br /> **[[Documentation/{{documentation/version}}/Modules/DTIImport|DTIImport]] {{wip}} (Sonia Pujol) <br /> **[[Documentation/{{documentation/version}}/Modules/DTIExport|DTIExport]] {{wip}} (Sonia Pujol)<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleToLabelMap|Fiber Bundle to Label Map]] (Steve Pieper)<br /> <br /> ==IGT==<br /> *[[Documentation/{{documentation/version}}/Modules/OpenIGTLinkIF| OpenIGTLink IF]] (Junichi Tokuda) {{wip}}<br /> <br /> ==Filtering==<br /> *[[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection | N4ITK Bias Field Correction]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/CheckerBoardFilter|CheckerBoard Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExtractSkeleton|Extract Skeleton]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/HistogramMatching|Histogram Matching]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ImageLabelCombine|Image Label Combine]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ResampleScalarVectorDWIVolume|Resample Scalar/Vector/DWI Volume]] (Francois Budin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ThresholdScalarVolume|Threshold Scalar Volume]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/VotingBinaryHoleFillingImageFilter|Voting Binary Hole Filling Image Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SimpleFilters|Simple Filters]] (Bradley Lowekamp) {{wip}}<br /> *Arithmetic <br /> **[[Documentation/{{documentation/version}}/Modules/AddScalarVolumes|Add Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/CastScalarVolume| Cast Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MaskScalarVolume| Mask Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MultiplyScalarVolumes| Multiply Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/SubtractScalarVolumes| Subtract Scalar Volumes]] (Jim Miller) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion |Gradient Anisotropic Diffusion]] (Jim Miller) {{wip}} <br /> **[[Documentation/{{documentation/version}}/Modules/CurvatureAnisotropicDiffusion|Curvature Anisotropic Diffusion]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/GaussianBlurImageFilter|Gaussian Blur Image Filter]] (Stephen Aylward) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MedianImageFilter|Median Image Filter]] (Jim Miller) {{wip}}<br /> *Morphology<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleFillHoleImageFilter|Grayscale Fill Hole Image Filter]] (Jim Miller)<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleGrindPeakImageFilter|Grayscale Grind Peak Image Filter]] (Jim Miller) {{wip}}<br /> <br /> ==Surface Models==<br /> *[[Documentation/{{documentation/version}}/Modules/GrayscaleModelMaker| Grayscale Model Maker]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/LabelMapSmoothing|Label Map Smoothing]] {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/MergeModels| Merge Models]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelMaker| Model Maker]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelToLabelMap| Model To LabelMap]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ProbeVolumeWithModel| Probe Volume With Model]] (Lauren O'Donnell) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SurfaceToolbox| SurfaceToolbox]] (Luca Antiga)<br /> <br /> ==Converters==<br /> *[[Documentation/{{documentation/version}}/Modules/CreateDICOMSeries|Create DICOM Series]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Crop Volume|Crop Volume]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DicomToNrrdConverter|Dicom to Nrrd Converter]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/OrientScalarVolume| Orient Scalar Volume]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/VectorToScalarVolume| Vector to Scalar Volume]] (Steve Pieper) <br /> {{wip}}<br /> <br /> ==Endoscopy==<br /> *[[Documentation/{{documentation/version}}/Modules/Endoscopy|Endoscopy]] (Steve Pieper)<br /> <br /> =={{wip}} Work in Progress {{wip}}==<br /> * MultiVolume Support<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeImporter|MultiVolume Importer]] (Andrey Fedorov)<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeExplorer|MultiVolume Explorer]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DataStore|Data Store]] (Charles Marion)<br /> <br /> ==Developer Tools==<br /> *[[Documentation/{{documentation/version}}/Modules/Cameras|Cameras]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/EventBroker|Event Broker]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExecutionModelTour |Execution Model Tour]] (JC Fillion-Robin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/YOURMODULENAME|Module Template]] (JC Fillion-Robin)<br /> *Performance Tests<br /> *Tractography<br /> <br /> ==Legacy==<br /> *Converters<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineToDeformationField|BSpline To Deformation Field]]<br /> *Diffusion<br /> **Denoising<br /> ***[[Documentation/{{documentation/version}}/Modules/DWIUnbiasedNonLocalMeansFilter|DWI Unbiased Non Local Means Filter]]<br /> *Filtering<br /> **[[Documentation/{{documentation/version}}/Modules/MRIBiasFieldCorrection|MRI Bias Field Correction]] (Sylvain Jaume) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdImageFilter|Otsu Threshold Image Filter]] (Bill Lorensen) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleScalarVolume|Resample Scalar Volume]] (Jim Miller) {{wip}}<br /> *Registration<br /> **[[Documentation/{{documentation/version}}/Modules/RigidRegistration|Rigid Registration]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/AffineRegistration|Affine Registration]] (Daniel Blezek) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineDeformableRegistration|BSpline Deformable Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ExpertAutomatedRegistration|Expert Automated Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/LinearRegistration|Linear Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MultiResolutionAffineRegistration|MultiResolution Affine Registration]] (Casey Goodlett){{wip}}<br /> &lt;!--<br /> **[[Documentation/{{documentation/version}}/Modules/TestGridTransformRegistration|Test Grid Transform Registration]] {{wip}}<br /> --&gt;<br /> *Segmentation<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdSegmentation|Otsu Threshold Segmentation]] (Bill Lorensen) {{wip}}</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules&diff=36339 2013-11-01T05:26:01Z

<p>Liangjiazhu: /* Segmentation */</p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> =Modules by Category=<br /> ==Core Modules==<br /> <br /> {{documentation/{{documentation/version}}/Developers/random-image}}<br /> <br /> *[[Documentation/{{documentation/version}}/Modules/Annotations|Annotations]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Colors|Colors]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Data|Data]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/DICOM|DICOM]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/Editor|Editor]] (Steve Pieper)<br /> **[[Documentation/{{documentation/version}}/Modules/Editor/WatershedFromMarkers|WatershedFromMarkers]] (Bradley Lowekamp) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Markups|Markups]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/Models|Models]] (Nicole Aucoin)<br /> *[[Documentation/{{documentation/version}}/Modules/SceneViews|SceneViews]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/Transforms|Transforms]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ViewControllers|View Controllers]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/Volumes|Volumes]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/VolumeRendering|Volume Rendering]] (Julien Finet)<br /> *[[Documentation/{{documentation/version}}/Modules/SlicerWelcome|Welcome to Slicer]] (Wendy Plesniak)<br /> <br /> ==Wizards==<br /> <br /> ==Informatics==<br /> *[[Documentation/{{documentation/version}}/Modules/SampleData|Sample Data]] (Steve Pieper)<br /> <br /> ==Registration==<br /> *[[:Category:Documentation/{{documentation/version}}/Modules/Registration|Introduction to Registration]] (Dominik Meier)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSFit|General Brainsfit Registration]] (Hans Johnson)<br /> *[[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample Image (BRAINS)]] (Hans Johnson)<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/ACPCTransform|ACPC Transform]] (Nicole Aucoin)<br /> **[[Documentation/{{documentation/version}}/Modules/BRAINSDemonWarp|Demon Registration (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/FiducialRegistration|Fiducial Registration]] (Stephen Aylward) {{wip}}<br /> **Vector Demon Registration (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/Reformat|Reformat]] (Michael Jeulin-Lagarrigue)<br /> **[[Documentation/{{documentation/version}}/Modules/SegmentationAidedRegistration|Segmentation Aided Registration]] (Yi Gao) {{wip}}<br /> <br /> ==Segmentation==<br /> *[[Documentation/{{documentation/version}}/Modules/EMSegment Easy|EMSegment Easy (no atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/EMSegmenter|EMSegment (with atlas)]] (Kilian Pohl) <br /> *[[Documentation/{{documentation/version}}/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]] (Jim Miller, Harini Veeraraghavan) <br /> *[[Documentation/{{documentation/version}}/Modules/KSliceInteractiveSegmenter|KSlice Interactive Segmenter]] (Ivan Kolesov, Peter Karasev, Patricio Vela, Allen Tannenbaum, Karl Fritscher, Steve Pieper )<br /> *Specialized<br /> **[[Documentation/{{documentation/version}}/Modules/EMSegment Command-line | EMSegment Command-line]] (Kilian Pohl)<br /> **[[Documentation/{{documentation/version}}/Modules/ForegroundMasking|Foreground Masking (BRAINS)]] (Hans Johnson)<br /> **[[Documentation/{{documentation/version}}/Modules/RobustStatisticsSegmenter|Robust Statistics Segmenter]] (Yi Gao)<br /> **[[Documentation/{{documentation/version}}/Modules/LAScarSegmenter|Left Atrial Scar Segmenter]] (Liangjia Zhu)<br /> **[[Documentation/{{documentation/version}}/Modules/LASegmenter|Left Atrium Segmenter]] (Liangjia Zhu)<br /> <br /> ==Quantification==<br /> *[[Documentation/{{documentation/version}}/Modules/DataProbe|Data Probe]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/LabelStatistics|Label Statistics]] (Steve Pieper)<br /> *[[Documentation/{{documentation/version}}/Modules/PETStandardUptakeValueComputation|PET Standard Uptake Value Computation]] (Nicole Aucoin)<br /> * Change quantification<br /> ** [[Documentation/{{documentation/version}}/Modules/IntensityDifferenceMetric|Intensity Difference Metric]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/SNR Measurement|SNR Measurement]] (Babak Matinfar)<br /> <br /> ==Diffusion== <br /> *DWI to Full Brain Tractography (Demian Wasserman) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/DWIJointRicianLMMSEFilter|DWI Joint Rician LMMSE Image Filter]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DWIRicianLMMSEFilter|DWI Rician LMMSE Filter]] (Demian Wasserman) {{wip}}<br /> *Tractography<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyDisplay|Tractography Display]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyFiducialSeeding|Tractography Interactive Seeding]] (Alex Yarmarkovich)<br /> **[[Documentation/{{documentation/version}}/Modules/TractographyLabelMapSeeding|Tractography Label Map Seeding]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/UKFTractography|UKF Tractography]]<br /> *Utilities<br /> **[[Documentation/{{documentation/version}}/Modules/DWIToDTIEstimation|DWI To DTI Estimation]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]] (Demian Wasserman) <br /> **[[Documentation/{{documentation/version}}/Modules/DiffusionWeightedVolumeMasking|Diffusion Weighted Volume Masking]] (Demian Wasserman) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleDTIVolume|Resample DTI Volume]] (Demian Wasserman) {{wip}}<br /> *Data Conversion<br /> **[[Documentation/{{documentation/version}}/Modules/DTIImport|DTIImport]] {{wip}} (Sonia Pujol) <br /> **[[Documentation/{{documentation/version}}/Modules/DTIExport|DTIExport]] {{wip}} (Sonia Pujol)<br /> **[[Documentation/{{documentation/version}}/Modules/FiberBundleToLabelMap|Fiber Bundle to Label Map]] (Steve Pieper)<br /> <br /> ==IGT==<br /> *[[Documentation/{{documentation/version}}/Modules/OpenIGTLinkIF| OpenIGTLink IF]] (Junichi Tokuda) {{wip}}<br /> <br /> ==Filtering==<br /> *[[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection | N4ITK Bias Field Correction]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/CheckerBoardFilter|CheckerBoard Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExtractSkeleton|Extract Skeleton]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/HistogramMatching|Histogram Matching]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ImageLabelCombine|Image Label Combine]] (Alex Yarmarkovich) <br /> *[[Documentation/{{documentation/version}}/Modules/ResampleScalarVectorDWIVolume|Resample Scalar/Vector/DWI Volume]] (Francois Budin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ThresholdScalarVolume|Threshold Scalar Volume]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/VotingBinaryHoleFillingImageFilter|Voting Binary Hole Filling Image Filter]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SimpleFilters|Simple Filters]] (Bradley Lowekamp) {{wip}}<br /> *Arithmetic <br /> **[[Documentation/{{documentation/version}}/Modules/AddScalarVolumes|Add Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/CastScalarVolume| Cast Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MaskScalarVolume| Mask Scalar Volume]] (Nicole Aucoin) <br /> **[[Documentation/{{documentation/version}}/Modules/MultiplyScalarVolumes| Multiply Scalar Volumes]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/SubtractScalarVolumes| Subtract Scalar Volumes]] (Jim Miller) {{wip}}<br /> *Denoising<br /> **[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion |Gradient Anisotropic Diffusion]] (Jim Miller) {{wip}} <br /> **[[Documentation/{{documentation/version}}/Modules/CurvatureAnisotropicDiffusion|Curvature Anisotropic Diffusion]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/GaussianBlurImageFilter|Gaussian Blur Image Filter]] (Stephen Aylward) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MedianImageFilter|Median Image Filter]] (Jim Miller) {{wip}}<br /> *Morphology<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleFillHoleImageFilter|Grayscale Fill Hole Image Filter]] (Jim Miller)<br /> **[[Documentation/{{documentation/version}}/Modules/GrayscaleGrindPeakImageFilter|Grayscale Grind Peak Image Filter]] (Jim Miller) {{wip}}<br /> <br /> ==Surface Models==<br /> *[[Documentation/{{documentation/version}}/Modules/GrayscaleModelMaker| Grayscale Model Maker]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/LabelMapSmoothing|Label Map Smoothing]] {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/MergeModels| Merge Models]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelMaker| Model Maker]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ModelToLabelMap| Model To LabelMap]] (Nicole Aucoin) <br /> *[[Documentation/{{documentation/version}}/Modules/ProbeVolumeWithModel| Probe Volume With Model]] (Lauren O'Donnell) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/SurfaceToolbox| SurfaceToolbox]] (Luca Antiga)<br /> <br /> ==Converters==<br /> *[[Documentation/{{documentation/version}}/Modules/CreateDICOMSeries|Create DICOM Series]] (Jim Miller) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/Crop Volume|Crop Volume]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DicomToNrrdConverter|Dicom to Nrrd Converter]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/OrientScalarVolume| Orient Scalar Volume]] (Jim Miller) <br /> *[[Documentation/{{documentation/version}}/Modules/VectorToScalarVolume| Vector to Scalar Volume]] (Steve Pieper) <br /> {{wip}}<br /> <br /> ==Endoscopy==<br /> *[[Documentation/{{documentation/version}}/Modules/Endoscopy|Endoscopy]] (Steve Pieper)<br /> <br /> =={{wip}} Work in Progress {{wip}}==<br /> * MultiVolume Support<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeImporter|MultiVolume Importer]] (Andrey Fedorov)<br /> **[[Documentation/{{documentation/version}}/Modules/MultiVolumeExplorer|MultiVolume Explorer]] (Andrey Fedorov)<br /> *[[Documentation/{{documentation/version}}/Modules/DataStore|Data Store]] (Charles Marion)<br /> <br /> ==Developer Tools==<br /> *[[Documentation/{{documentation/version}}/Modules/Cameras|Cameras]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/EventBroker|Event Broker]] (Julien Finet) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/ExecutionModelTour |Execution Model Tour]] (JC Fillion-Robin) {{wip}}<br /> *[[Documentation/{{documentation/version}}/Modules/YOURMODULENAME|Module Template]] (JC Fillion-Robin)<br /> *Performance Tests<br /> *Tractography<br /> <br /> ==Legacy==<br /> *Converters<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineToDeformationField|BSpline To Deformation Field]]<br /> *Diffusion<br /> **Denoising<br /> ***[[Documentation/{{documentation/version}}/Modules/DWIUnbiasedNonLocalMeansFilter|DWI Unbiased Non Local Means Filter]]<br /> *Filtering<br /> **[[Documentation/{{documentation/version}}/Modules/MRIBiasFieldCorrection|MRI Bias Field Correction]] (Sylvain Jaume) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdImageFilter|Otsu Threshold Image Filter]] (Bill Lorensen) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ResampleScalarVolume|Resample Scalar Volume]] (Jim Miller) {{wip}}<br /> *Registration<br /> **[[Documentation/{{documentation/version}}/Modules/RigidRegistration|Rigid Registration]] (Jim Miller) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/AffineRegistration|Affine Registration]] (Daniel Blezek) {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/BSplineDeformableRegistration|BSpline Deformable Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/ExpertAutomatedRegistration|Expert Automated Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/LinearRegistration|Linear Registration]] {{wip}}<br /> **[[Documentation/{{documentation/version}}/Modules/MultiResolutionAffineRegistration|MultiResolution Affine Registration]] (Casey Goodlett){{wip}}<br /> &lt;!--<br /> **[[Documentation/{{documentation/version}}/Modules/TestGridTransformRegistration|Test Grid Transform Registration]] {{wip}}<br /> --&gt;<br /> *Segmentation<br /> **[[Documentation/{{documentation/version}}/Modules/OtsuThresholdSegmentation|Otsu Threshold Segmentation]] (Bill Lorensen) {{wip}}</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/LASegmenter&diff=36338 2013-11-01T05:24:26Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completely inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedPanel.png|350px]][[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several minutes to process one volume<br /> [[file:LASegPanel.png|350px]][[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> A properly set seed region is required to start the segmentation process. In principle, the seed region needs to be set closely to the center of the left atrium and covers the most of left atrium region on the selected image slice. The radius of a seed region is determined by the size of the left atrium. A small radius may cause the segmentation terminates before reaching the left atrium boundary. A large radius may lead to oversegmentation. Examples of good seed regions with various radius from small to large are shown below: <br /> <br /> [[file:LASeedRadSmall.png|300px]] [[file:LASeedRadMid.png|300px]] [[file:LASeedRadLarge.png|300px]]<br /> <br /> From left to right: seed radii are 10, 18, and 24 mm, respectively. <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/4.3/Modules/LASegmenter&diff=36337 2013-11-01T05:23:36Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completely inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedPanel.png|350px]][[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several minutes to process one volume<br /> [[file:LASegPanel.png|350px]][[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> A properly set seed region is required to start the segmentation process. In principle, the seed region needs to be set closely to the center of the left atrium and covers the most of left atrium region on the selected image slice. The radius of a seed region is determined by the size of the left atrium. A small radius may cause the segmentation terminates before reaching the left atrium boundary. A large radius may lead to oversegmentation. Examples of good seed regions with various radius from small to large are shown below: <br /> <br /> [[file:LASeedRadSmall.png|300px]] [[file:LASeedRadMid.png|300px]] [[file:LASeedRadLarge.png|300px]]<br /> <br /> From left to right: seed radii are 10, 18, and 24 mm, respectively. <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/LASegmenter&diff=36336 2013-11-01T05:22:12Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completely inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedPanel.png|350px]][[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several minutes to process one volume<br /> [[file:LASegPanel.png|350px]][[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> A properly set seed region is required to start the segmentation process. In principle, the seed region needs to be set closely to the center of the left atrium and covers the most of left atrium region on the selected image slice. The radius of a seed region is determined by the size of the left atrium. A small radius may cause the segmentation terminates before reaching the left atrium boundary. A large radius may lead to oversegmentation. Examples of good seed regions with various radius from small to large are shown below. <br /> <br /> [[file:LASeedRadSmall.png|300px]] [[file:LASeedRadMid.png|300px]] [[file:LASeedRadLarge.png|300px]]<br /> <br /> From left to right: seed radii are 10, 18, and 24 mm, respectively. <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/LASegmenter&diff=36335 2013-11-01T05:17:49Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completely inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedPanel.png|350px]][[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several minutes to process one volume<br /> [[file:LASegPanel.png|350px]][[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> A properly set seed region is required to start the segmentation process. In principle, the seed region needs to be set closely to the center of the left atrium and covers the most of left atrium region on the selected image slice. Examples of good seed regions with various radius from small to large are shown below. <br /> <br /> [[file:LASeedRadSmall.png|300px]] [[file:LASeedRadMid.png|300px]] [[file:LASeedRadLarge.png|300px]]<br /> <br /> From left to right: seed radii are 10, 18, and 24 mm, respectively. <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/LASegmenter&diff=36334 2013-11-01T05:14:59Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completely inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedPanel.png|350px]][[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several minutes to process one volume<br /> [[file:LASegPanel.png|350px]][[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> A properly set seed region is required to start the segmentation process. In principle, the seed region needs to be set closely to the center of the left atrium and covers the most of left atrium region in the axial view. Examples of good seed regions with variant radii are shown below. <br /> [[file:LASeedRadSmall.png|300px]] [[file:LASeedRadMid.png|300px]] [[file:LASeedRadLarge.png|300px]]<br /> From left to right: seed radii are 10, 18, and 24 mm, respectively. <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/LASegmenter&diff=36333 2013-11-01T05:00:39Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completely inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedPanel.png|350px]][[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several minutes to process one volume<br /> [[file:LASegPanel.png|350px]][[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/LASegmenter&diff=36332 2013-11-01T04:55:25Z

<p>Liangjiazhu: </p> <hr /> <div>&lt;noinclude&gt;{{documentation/versioncheck}}&lt;/noinclude&gt;<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-header}}<br /> &lt;!-- ---------------------------- --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br /> {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].&lt;br&gt;<br /> <br /> Contributor: Liangjia Zhu, Stony Brook University &lt;br&gt;<br /> Contributor: Yi Gao, University of Alabama at Birmingham &lt;br&gt;<br /> Contributor: Josh Cates, University of Utah &lt;br&gt;<br /> Contributor: Alan Morris, University of Utah &lt;br&gt;<br /> Contributor: Danny Perry, University of Utah &lt;br&gt;<br /> Contributor: Greg Gardner, University of Utah &lt;br&gt;<br /> Contributor: Rob MacLeod, University Utah &lt;br&gt;<br /> Contributor: Allen Tannenbaum, Stony Brook University &lt;br&gt;<br /> Contact: Liangjia Zhu, &lt;email&gt;liangjia.zhu@stonybrook.edu&lt;/email&gt;&lt;br&gt;<br /> {{documentation/{{documentation/version}}/module-introduction-row}}<br /> {{documentation/{{documentation/version}}/module-introduction-logo-gallery<br /> |{{collaborator|logo|namic}}|{{collaborator|longname|namic}}<br /> |{{collaborator|logo|uab}}|{{collaborator|longname|uab}}<br /> |{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}<br /> |{{collaborator|logo|sci}}|{{collaborator|longname|sci}}<br /> }}<br /> {{documentation/{{documentation/version}}/module-introduction-end}}<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Module Description}}<br /> This module performs the segmentation of the left atrium from MR images. It is an implementation of a region-growing-based method with a shape prior. The module starts from a manually generated seed region inside the left atrium. Then, the growing process gradually captures the whole left atrium region.<br /> &lt;!--<br /> Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br /> <br /> If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:&lt;pre&gt;{{documentation/{{documentation/version}}/module-description}}<br /> <br /> {{documentation/{{documentation/version}}/module-description}}<br /> --&gt;<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Use Cases}}<br /> This module is specifically designed for the segmentation of the left atrium from MR images.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Tutorials}}<br /> *Load source image &lt;br /&gt;[[file:LASegLoad.png]]<br /> &lt;br /&gt;<br /> <br /> *Select Editor module<br /> # Choose Master Volume and Merge Volume (label map to be used for the left atrium seed region)<br /> # Select PaintEffect and adjust Radius (in mm) so that the painted seed region (a circle) is completetly inside the left atrium<br /> # Go to the Axial view, choose a slice that is approximately to the center of the left atrium. Then, draw a circle that lies to the center of the left atrium on the slice<br /> [[file:LASegSeedImage.png]]<br /> <br /> &lt;br /&gt;<br /> *Go to Segmentation-&gt;Left Atrium Segmentation module<br /> # Select Original Image, Seed Label Image (the one obtained from the Editor module), and select/create Segmentation Results<br /> # Adjust parameters if needed<br /> # Press Apply button and it takes several mimutes to process one volume<br /> [[file:LASegResult.png]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Panels and their use}}<br /> *Seed Editor Panel<br /> [[file:LASegSeedPanel.png|350px]]<br /> ** Master Volume: the image to be segmented<br /> ** <br /> * Segmenter Panel<br /> [[file:LASegPanel.png|350px]]<br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Similar Modules}}<br /> *[[Documentation/Nightly/Extensions/CardiacMRIToolkit|CARMA]]<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|References}}<br /> L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.<br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-section|Information for Developers}}<br /> {{documentation/{{documentation/version}}/module-developerinfo}}<br /> <br /> <br /> &lt;!-- ---------------------------- --&gt;<br /> {{documentation/{{documentation/version}}/module-footer}}<br /> &lt;!-- ---------------------------- --&gt;</div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:LASeedRadLarge.png&diff=36331 2013-11-01T04:52:48Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu https://www.slicer.org/w/index.php?title=File:LASeedRadMid.png&diff=36330 2013-11-01T04:52:31Z

<p>Liangjiazhu: </p> <hr /> <div></div>

Liangjiazhu