Slicer Wiki - User contributions [en]

Documentation/4.4/Modules/SegmentationAidedRegistration

2015-10-24T17:23:33Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
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{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:AfibMIAffineRegistrationWithContour.png|Affine registration using MI metric of the two LGE-MR images, with the red contour showing the left atrium. Top: fixed image; Middle: registered moving image; Bottom: putting them together. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the change of LA at those two time points, foucs has been put on the LA region.
Image:AfibSegmentationAidedRegistration.png|Segmentation aided registration using this module. The overall registration performance for the entire volume is bad, even worse than before registration.
Image:AfibSegmentationAidedRegistrationContour.png|Segmentation aided registration using this module. But at the target (left atrium) region, its very accurate, enabling analysis of the target region.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
* Step 1. Give the input images, including:
** Fixed grayscale image
** Segmentation label image for the target region in the fixed image
** Moving grayscale image
** Segmentation label image for the target region in the moving image
* Step 2. Give the output filename.
* Run
* There is an optional parameter:
** Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>
* Input
** Fixed grayscale image
** Segmentation label image for the target region in the fixed image
** Moving grayscale image
** Segmentation label image for the target region in the moving image
* Output
** Registered moving image
* Parameter (pptional)
** Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/Nightly/Modules/SlicerModuleCardiacRegistration]]


{{documentation/{{documentation/version}}/module-section|References}}
* Y Gao, L Zhu, J Cates, R.S. MacLeod, S Bouix, A Tannenbaum; [http://epubs.siam.org/doi/pdf/10.1137/130933423 A Kalman Filtering Perspective for Multiatlas Segmentation]; SIAM Journal on Imaging Sciences 8, no. 2 (2015): 1007-1029.
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2015-10-24T17:22:11Z

Ygao:

Documentation/Nightly/Modules/RobustStatisticsSegmenter

2013-11-05T23:32:33Z

Ygao: copy from 4.3 doc for newly added rejection label feature and re-formatting

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
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}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, Meningioma segmentation from MRI''':
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, Change target volume limit to 100cc. And run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
</center>

* '''Use Case 2, Right kidney segmentation from CT image''':
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="700px" heights="400px">
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>
</center>
* '''Use Case 3, Left kidney segmentation from CT image''':
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="700px" heights="400px">
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>
</center>
* '''Use Case 4, Lung segmentation from CT image''':
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="700px" heights="400px">
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>
</center>
* '''Use Case 5, Left caudate segmentation from MR image''':
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip
** parameters:
*** Intensity homogeneity: 0.1
*** Boundary smoothness: 0.2
<center>
<gallery widths="400px" heights="250px" perrow="3">
Image:RSSLeftCaudateLeakage.png| RSS segments left caudate, but have leakage pointed out by the crosses
Image:RSSLeftCaudateRejectionLabel.png| Edit the seed label image and put label-2 at the leakage spot (yellow), keep the targe seed (green) un-changed, run again
Image:RSSLeftCaudateRejectionLabelNoLeakage.png| No leakage.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/4.3/Modules/RobustStatisticsSegmenter

2013-11-05T21:42:57Z

Ygao: use case 5 of rejection label

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, Meningioma segmentation from MRI''':
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, Change target volume limit to 100cc. And run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
</center>

* '''Use Case 2, Right kidney segmentation from CT image''':
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="700px" heights="400px">
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>
</center>
* '''Use Case 3, Left kidney segmentation from CT image''':
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="700px" heights="400px">
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>
</center>
* '''Use Case 4, Lung segmentation from CT image''':
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="700px" heights="400px">
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>
</center>
* '''Use Case 5, Left caudate segmentation from MR image''':
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip
** parameters:
*** Intensity homogeneity: 0.1
*** Boundary smoothness: 0.2
<center>
<gallery widths="400px" heights="250px" perrow="3">
Image:RSSLeftCaudateLeakage.png| RSS segments left caudate, but have leakage pointed out by the crosses
Image:RSSLeftCaudateRejectionLabel.png| Edit the seed label image and put label-2 at the leakage spot (yellow), keep the targe seed (green) un-changed, run again
Image:RSSLeftCaudateRejectionLabelNoLeakage.png| No leakage.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:RSSLeftCaudateRejectionLabelNoLeakage.png

2013-11-05T21:39:51Z

Ygao:

File:RSSLeftCaudateRejectionLabel.png

2013-11-05T21:38:46Z

Ygao:

File:RSSLeftCaudateLeakage.png

2013-11-05T21:37:52Z

Ygao:

Documentation/4.3/Modules/RobustStatisticsSegmenter

2013-11-05T21:10:30Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, Meningioma segmentation from MRI''':
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, Change target volume limit to 100cc. And run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
</center>

* '''Use Case 2, Right kidney segmentation from CT image''':
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="800px" heights="400px">
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>
</center>
* '''Use Case 3, Left kidney segmentation from CT image''':
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="800px" heights="400px">
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>
</center>
* '''Use Case 4, Lung segmentation from CT image''':
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="800px" heights="400px">
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>
</center>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/4.3/Modules/RobustStatisticsSegmenter

2013-11-05T21:08:57Z

Ygao: change figure display parameters

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, Change target volume limit to 100cc. And run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
</center>

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="800px" heights="400px">
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>
</center>
* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="800px" heights="400px">
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>
</center>
* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<center>
<gallery widths="800px" heights="400px">
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>
</center>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/4.3/Modules/RobustStatisticsSegmenter

2013-11-05T21:05:14Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, Change target volume limit to 100cc. And run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
</center>

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>

* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>

* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:MeningiomaRSSPanel.png

2013-11-05T21:04:25Z

Ygao: uploaded a new version of "File:MeningiomaRSSPanel.png"

Documentation/4.3/Modules/RobustStatisticsSegmenter

2013-11-05T20:58:05Z

Ygao: case 1 format changed

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, and run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
</center>
** parameters:
*** Intensity homogeneity: 0.5
*** Boundary smoothness: 0.5

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>

* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>

* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:RSSPanelSlicer4.png

2013-11-05T20:55:16Z

Ygao: uploaded a new version of "File:RSSPanelSlicer4.png"

panel of RSS in version 4

Documentation/4.3/Modules/RobustStatisticsSegmenter

2013-11-05T20:46:54Z

Ygao: edit contributors

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor: Ron Kikinis, BWH 
Contributor: Marc Niethammer, UNC 
Contributor: Sylvain Bouix, BWH 
Contributor: Martha Shenton, BWH 
Contributor: Allen Tannenbaum, SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<gallery>
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, and run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
** parameters:
*** Intensity homogeneity: 0.5
*** Boundary smoothness: 0.5

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>

* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>

* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/RobustStatisticsSegmenter

2013-09-09T19:37:34Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor1: Ron Kikinis, BWH 
Contributor2: Sylvain Bouix, BWH 
Contributor3: Martha Shenton, BWH 
Contributor4: Allen Tannenbaum: SBU 
Contact: Yi Gao, <email> gaoyi@uab.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|Image:Logo-splnew.jpg|Surgical Planning Laboratory
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<gallery>
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, and run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
** parameters:
*** Intensity homogeneity: 0.5
*** Boundary smoothness: 0.5

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>

* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>

* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/RobustStatisticsSegmenter

2013-09-09T19:32:07Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, UAB 
Contributor1: Ron Kikinis, BWH 
Contributor2: Sylvain Bouix, BWH 
Contributor3: Martha Shenton, BWH 
Contributor4: Allen Tannenbaum: SBU 
Contact: Yi Gao, <email> gaoyi@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|Image:Logo-splnew.jpg|Surgical Planning Laboratory
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<gallery>
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, and run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
** parameters:
*** Intensity homogeneity: 0.5
*** Boundary smoothness: 0.5

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>

* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>

* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T21:03:11Z

Ygao: tutorial

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:AfibMIAffineRegistrationWithContour.png|Affine registration using MI metric of the two LGE-MR images, with the red contour showing the left atrium. Top: fixed image; Middle: registered moving image; Bottom: putting them together. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the change of LA at those two time points, foucs has been put on the LA region.
Image:AfibSegmentationAidedRegistration.png|Segmentation aided registration using this module. The overall registration performance for the entire volume is bad, even worse than before registration.
Image:AfibSegmentationAidedRegistrationContour.png|Segmentation aided registration using this module. But at the target (left atrium) region, its very accurate, enabling analysis of the target region.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
* Step 1. Give the input images, including:
** Fixed grayscale image
** Segmentation label image for the target region in the fixed image
** Moving grayscale image
** Segmentation label image for the target region in the moving image
* Step 2. Give the output filename.
* Run
* There is an optional parameter:
** Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>
* Input
** Fixed grayscale image
** Segmentation label image for the target region in the fixed image
** Moving grayscale image
** Segmentation label image for the target region in the moving image
* Output
** Registered moving image
* Parameter (pptional)
** Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/Nightly/Modules/SlicerModuleCardiacRegistration]]


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T21:01:11Z

Ygao: panel and parameters

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:AfibMIAffineRegistrationWithContour.png|Affine registration using MI metric of the two LGE-MR images, with the red contour showing the left atrium. Top: fixed image; Middle: registered moving image; Bottom: putting them together. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the change of LA at those two time points, foucs has been put on the LA region.
Image:AfibSegmentationAidedRegistration.png|Segmentation aided registration using this module. The overall registration performance for the entire volume is bad, even worse than before registration.
Image:AfibSegmentationAidedRegistrationContour.png|Segmentation aided registration using this module. But at the target (left atrium) region, its very accurate, enabling analysis of the target region.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>
* Input
** Fixed grayscale image
** Segmentation label image for the target region in the fixed image
** Moving grayscale image
** Segmentation label image for the target region in the moving image
* Output
** Registered moving image
* Parameter (pptional)
** Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/Nightly/Modules/SlicerModuleCardiacRegistration]]


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T20:44:23Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:AfibMIAffineRegistrationWithContour.png|Affine registration using MI metric of the two LGE-MR images, with the red contour showing the left atrium. Top: fixed image; Middle: registered moving image; Bottom: putting them together. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the change of LA at those two time points, foucs has been put on the LA region.
Image:AfibSegmentationAidedRegistration.png|Segmentation aided registration using this module. The overall registration performance for the entire volume is bad, even worse than before registration.
Image:AfibSegmentationAidedRegistrationContour.png|Segmentation aided registration using this module. But at the target (left atrium) region, its very accurate, enabling analysis of the target region.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:AfibSegmentationAidedRegistrationContour.png

2013-06-18T20:44:16Z

Ygao:

File:AfibSegmentationAidedRegistration.png

2013-06-18T20:34:12Z

Ygao:

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T20:04:19Z

Ygao: gallery resize

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="400px" heights="400px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:AfibMIAffineRegistrationWithContour.png|Affine registration using MI metric of the two LGE-MR images, with the red contour showing the left atrium. Top: fixed image; Middle: registered moving image; Bottom: putting them together. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the change of LA at those two time points, foucs has been put on the LA region.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T20:03:07Z

Ygao: gray scale image registration with contour

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="500px" heights="300px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:AfibMIAffineRegistrationWithContour.png|Affine registration using MI metric of the two LGE-MR images, with the red contour showing the left atrium. Top: fixed image; Middle: registered moving image; Bottom: putting them together. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the change of LA at those two time points, foucs has been put on the LA region.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:AfibMIAffineRegistrationWithContour.png

2013-06-18T20:01:09Z

Ygao: affine registration using MI metric of two LGE MRI. The red contour is the left atrium. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the LA, this is not good enough.

affine registration using MI metric of two LGE MRI. The red contour is the left atrium. We see that the overall registration is good, but the left atrium position is not accurate. In the case where we want to study the LA, this is not good enough.

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T16:05:53Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
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|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery widths="500px" heights="300px" perrow="3">
Image:AfibBeforeRegistrationSmall.png|Register two LGE-MR images. Top: fixed image; Middle: moving image; Bottom: putting them together.
Image:AfibMIAffineRegistration.png|Affine registration using MI metric of the two LGE-MR images. Top: fixed image; Middle: registered moving image; Bottom: putting them together.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery caption="Module panel" widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:AfibMIAffineRegistration.png

2013-06-18T15:10:49Z

Ygao: Affine registration using MI metric of two MR images.

Affine registration using MI metric of two MR images.

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T15:09:05Z

Ygao: afib case, before registration

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T15:07:06Z

Ygao: resize panel figure

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery caption="GUI of module and one result" widths="500px" heights="300px" perrow="2">
Image:SegmentationAidedRegistrationUsageScreenShot.png|GUI with the result of one case for cardiac image: The red surface/contour is the segmentation of the left atrium (LA) of the pre-op MRI. It is overlayed on the registered post-op MRI. See the accurate matching of the LA.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery widths="400px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T15:06:18Z

Ygao:

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery caption="GUI of module and one result" widths="500px" heights="300px" perrow="2">
Image:SegmentationAidedRegistrationUsageScreenShot.png|GUI with the result of one case for cardiac image: The red surface/contour is the segmentation of the left atrium (LA) of the pre-op MRI. It is overlayed on the registered post-op MRI. See the accurate matching of the LA.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery widths="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T15:05:48Z

Ygao: resize panel figure

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery caption="GUI of module and one result" widths="500px" heights="300px" perrow="2">
Image:SegmentationAidedRegistrationUsageScreenShot.png|GUI with the result of one case for cardiac image: The red surface/contour is the segmentation of the left atrium (LA) of the pre-op MRI. It is overlayed on the registered post-op MRI. See the accurate matching of the LA.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:SegmentationAidedRegistrationUsageScreenShot.png

2013-06-18T15:05:15Z

Ygao: uploaded a new version of "File:SegmentationAidedRegistrationUsageScreenShot.png"

Screenshot of SegmentationAidedRegistration extension

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-18T15:04:07Z

Ygao: move module panel to Panels section

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery caption="GUI of module and one result" widths="500px" heights="300px" perrow="2">
Image:SegmentationAidedRegistrationUsageScreenShot.png|GUI with the result of one case for cardiac image: The red surface/contour is the segmentation of the left atrium (LA) of the pre-op MRI. It is overlayed on the registered post-op MRI. See the accurate matching of the LA.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
<gallery widths="300px" heights="400px">
Image:SegmentationAidedRegistrationUsageScreenShot.png|Module Panel.
</gallery>




{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:AfibBeforeRegistrationSmall.png

2013-06-18T13:46:30Z

Ygao: two heart MRI images, axial, which are to be registered. Top: fixed image Middle: moving image Bottom: putting them together.

two heart MRI images, axial, which are to be registered.
Top: fixed image
Middle: moving image
Bottom: putting them together.

File:SegmentationAidedRegistrationUsageScreenShot.png

2013-06-18T02:48:45Z

Ygao: uploaded a new version of "File:SegmentationAidedRegistrationUsageScreenShot.png": New GUI of the module. Showing the advanced parameter which allows the control over using only part of the image (fast) or the entire image (SLOW).

Screenshot of SegmentationAidedRegistration extension

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-14T14:49:50Z

Ygao: description of use case images, and put them to center

<noinclude>{{documentation/versioncheck}}</noinclude>

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
<center>
<gallery caption="GUI of module and one result" widths="500px" heights="300px" perrow="2">
Image:SegmentationAidedRegistrationUsageScreenShot.png|GUI with the result of one case for cardiac image: The red surface/contour is the segmentation of the left atrium (LA) of the pre-op MRI. It is overlayed on the registered post-op MRI. See the accurate matching of the LA.
Image:SegmentationAidedRegistrationComparison.png|Results. Top row: Pre-op LGE-MRI with its segmentation of the left atrium (LA). Middle row: Pre-op LA contour overlayed on the registered Post-op LGE-MRI. See the matching of the LA region. Bottom row: checker-board view of Pre-op and registered Post-op LGE-MRIs: they match well at the LA region but badly otherwise, indicating the necessity of using segmentation to aid the registration.
</gallery>
</center>

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-14T14:33:22Z

Ygao: change images into gallery

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-13T23:42:41Z

Ygao:

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
[[Image:SegmentationAidedRegistrationUsageScreenShot.png|800px]]

[[Image:SegmentationAidedRegistrationComparison.png|800px]]

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A

TODO: add a grid-view putting the lge46 and registered72. Apparently using grayscale image registration will/should not get the two image misalligned as a whole like that.


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-13T23:42:25Z

Ygao:

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
[[Image:SegmentationAidedRegistrationUsageScreenShot.png|800px]]
[[Image:SegmentationAidedRegistrationComparison.png|800px]]

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A

TODO: add a grid-view putting the lge46 and registered72. Apparently using grayscale image registration will/should not get the two image misalligned as a whole like that.


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

File:SegmentationAidedRegistrationComparison.png

2013-06-13T23:41:41Z

Ygao:

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-13T23:11:11Z

Ygao:

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
[[Image:SegmentationAidedRegistrationUsageScreenShot.png|800px]]

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A

TODO: add a grid-view putting the lge46 and registered72. Apparently using grayscale image registration will/should not get the two image misalligned as a whole like that.


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/RobustStatisticsSegmenter

2013-06-08T03:26:51Z

Ygao:

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 
Author: Yi Gao, Georgia Tech 
Contributor1: Allen Tannenbaum (Author): Georgia Tech 
Contributor2: Ron Kikinis, BWH 
Contact: Yi Gao, <email> yi.gao@gatech.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|Image:GeorgiaTech-logo.png|Georgia Tech
|Image:Logo-splnew.jpg|Surgical Planning Laboratory
}}
{{documentation/{{documentation/version}}/module-introduction-end}}

{{documentation/{{documentation/version}}/module-section|Module Description}}
{{documentation/{{documentation/version}}/module-description}}
This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
Most frequently used for these scenarios:

* '''Use Case 1, meningioma''':
** Segment meningioma from MRI
** Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case1/grayscale.nrrd
** Steps to get the segmentation:
<gallery>
Image:MeningiomaCase1RSSLabel.png|Drawn some seeds in the editor module
Image:MeningiomaRSSPanel.png|Set up in RSS module panel, and run
Image:MeningiomaCase1.png|RSS results for extracting meningioma
</gallery>
** parameters:
*** Intensity homogeneity: 0.5
*** Boundary smoothness: 0.5

* '''Use Case 2, right kidney, CT image''':
** Segment right kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyRightCTRSS.png| RSS segments right kidney from CT image
</gallery>

* '''Use Case 3, left kidney, CT image''':
** Segment left kidney from CT image
** Test case file [[File:CT_liver_segmentation_case.tgz]]
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:KidneyLeftCTRSS.png| RSS segments left kidney from CT image
</gallery>

* '''Use Case 4, Lung, CT image''':
** Segment lung from CT image
** Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
** parameters:
*** Intensity homogeneity: 0.7
*** Boundary smoothness: 0.4
<gallery>
Image:LUNGIXRSSSegmentation.png| RSS segments lung from CT image
</gallery>


{{documentation/{{documentation/version}}/module-section|Tutorials}}

* First run:
# Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
# Run the module using the default parameters.

* Note:
# The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
# The positions of the seeds have to be in the object, preferably close to center.

* Troubleshooting
** '''Surface is too rough.''' Try:
*** Increase "Boundary smoothness"
** '''Leakage into thin/narrow regions'''. Try:
*** Increase "Boundary smoothness"
** '''leakage into similar (but still different) intensity regions (which is not necessarily thin)''', Try:
*** Increase "Intensity homogeneity"
** '''Some regions are missed''': Try (either one):
*** Increase "Max volume"
*** Decrease"Intensity homogeneity"
*** Decrease "Boundary smoothness"
** '''Some regions are missed, at the same time leakages to some other regions'''. Try (either one)
*** Increase "Intensity homogeneity"
*** Add some other seeds


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
{|
|
* '''Parameters panel:'''
** '''Approximate volume:''' The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
** '''Intensity homogeneity:''' If the target contains homogeneous intensity, then give a close-to-1 value here.
** '''Boundary smoothness:''' Larger value will result in smoother boundary and a more spherical looking result.
** '''Output Label Value:''' Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
** '''Max running time:''' The upper limit for program running time.
* '''IO panel:'''
** '''Input Image:''' The image to be segmented.
** '''Label Image:''' The label map providing initial seeds.
* '''Output Volume:''' The output volumetric image.
|[[Image:RSSPanelSlicer4.png|thumb|280px|User Interface]]
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/4.0/Modules/SimpleRegionGrowingSegmentation|Simple Region Growing Segmentation]]
* GrowCut in the editor module


{{documentation/{{documentation/version}}/module-section|References}}
Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-08T03:24:11Z

Ygao:

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Contributor: Yi Gao, Brigham and Women's Hospital 
Contributor: Josh Cates, University of Utah 
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor: Alan Morris, University of Utah 
Contributor: Danny Perry, University of Utah 
Contributor: Greg Gardner, University of Utah 
Contributor: Rob MacLeod, University Utah 
Contributor: Sylvain Bouix, Brigham and Women's Hospital 
Contributor: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
[[Image:SegmentationAidedRegistrationUsageScreenShot.png|800px]]

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-08T03:04:22Z

Ygao: a figure showing the afib use case, will populate text

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Author: Yi Gao, Brigham and Women's Hospital 
Contributor1: Josh Cates, University of Utah 
Contributor2: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor3: Alan Morris, University of Utah 
Contributor4: Danny Perry, University of Utah 
Contributor5: Greg Gardner, University of Utah 
Contributor6: Rob MacLeod, University Utah 
Contributor7: Sylvain Bouix, Brigham and Women's Hospital 
Contributor8: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.
[[Image:SegmentationAidedRegistrationUsageScreenShot.png|800px]]

{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-08T03:02:08Z

Ygao: added uab collaborator

{{documentation/{{documentation/version}}/module-header}}



{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
{{documentation/{{documentation/version}}/module-introduction-row}}
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]. 

Author: Yi Gao, Brigham and Women's Hospital 
Contributor1: Josh Cates, University of Utah 
Contributor2: Liang-Jia Zhu, University of Alabama at Birmingham 
Contributor3: Alan Morris, University of Utah 
Contributor4: Danny Perry, University of Utah 
Contributor5: Greg Gardner, University of Utah 
Contributor6: Rob MacLeod, University Utah 
Contributor7: Sylvain Bouix, Brigham and Women's Hospital 
Contributor8: Allen Tannenbaum, University of Alabama at Birmingham 
Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email> 
{{documentation/{{documentation/version}}/module-introduction-row}}
{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
|{{collaborator|logo|pnl}}|{{collaborator|longname|pnl}}
|{{collaborator|logo|uab}}|{{collaborator|longname|uab}}
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}}
|{{collaborator|logo|sci}}|{{collaborator|longname|sci}}
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.



{{documentation/{{documentation/version}}/module-section|Use Cases}}
* In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.


{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
N/A



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
N/A


{{documentation/{{documentation/version}}/module-section|References}}
* 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
* 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.


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


{{documentation/{{documentation/version}}/module-footer}}

Template:Collaborator

2013-06-08T03:01:35Z

Ygao: .png added for figure

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|}}</includeonly><noinclude>

== Usage ==

{{collaborator/usage-start}}
{{collaborator/usage-row|afrl}}
{{collaborator/usage-row|bsf}}
{{collaborator/usage-row|cco}}
{{collaborator/usage-row|csail}}
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{{collaborator/usage-row|itk}}
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{{collaborator/usage-row|lmi}}
{{collaborator/usage-row|martinos}}
{{collaborator/usage-row|nac}}
{{collaborator/usage-row|namic}}
{{collaborator/usage-row|ncigt}}
{{collaborator/usage-row|nedo}}
{{collaborator/usage-row|pnl}}
{{collaborator/usage-row|python}}
{{collaborator/usage-row|sci}}
{{collaborator/usage-row|slicer4}}
{{collaborator/usage-row|sparkit}}
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{{collaborator/usage-end}}

[[Category:Templates|{{PAGENAME}}]]
[[Category:Templates:Logos]]
</noinclude>

Template:Collaborator

2013-06-08T03:00:22Z

Ygao: add uab logo

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|}}</includeonly><noinclude>

== Usage ==

{{collaborator/usage-start}}
{{collaborator/usage-row|afrl}}
{{collaborator/usage-row|bsf}}
{{collaborator/usage-row|cco}}
{{collaborator/usage-row|csail}}
{{collaborator/usage-row|ctk}}
{{collaborator/usage-row|ge}}
{{collaborator/usage-row|georgiatech}}
{{collaborator/usage-row|isomics}}
{{collaborator/usage-row|itk}}
{{collaborator/usage-row|kitware}}
{{collaborator/usage-row|lmi}}
{{collaborator/usage-row|martinos}}
{{collaborator/usage-row|nac}}
{{collaborator/usage-row|namic}}
{{collaborator/usage-row|ncigt}}
{{collaborator/usage-row|nedo}}
{{collaborator/usage-row|pnl}}
{{collaborator/usage-row|python}}
{{collaborator/usage-row|sci}}
{{collaborator/usage-row|slicer4}}
{{collaborator/usage-row|sparkit}}
{{collaborator/usage-row|spl}}
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{{collaborator/usage-row|uiowa}}
{{collaborator/usage-row|unc}}
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{{collaborator/usage-end}}

[[Category:Templates|{{PAGENAME}}]]
[[Category:Templates:Logos]]
</noinclude>

File:UAB MONOGRAM.png

2013-06-08T02:58:16Z

Ygao:

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-08T02:56:52Z

Ygao: add pnl collaborator logo

Template:Collaborator

2013-06-08T02:53:31Z

Ygao: Try to add PNL but preview is not successful... Will save the current status and ask Jc if there's anything I have done wrong.

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|}}{{#ifeq: {{{2|}}} | ctk |
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|}}{{#ifeq: {{{2|}}} | kitware |
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|}}</includeonly><noinclude>

== Usage ==

{{collaborator/usage-start}}
{{collaborator/usage-row|afrl}}
{{collaborator/usage-row|bsf}}
{{collaborator/usage-row|cco}}
{{collaborator/usage-row|csail}}
{{collaborator/usage-row|ctk}}
{{collaborator/usage-row|ge}}
{{collaborator/usage-row|georgiatech}}
{{collaborator/usage-row|isomics}}
{{collaborator/usage-row|itk}}
{{collaborator/usage-row|kitware}}
{{collaborator/usage-row|lmi}}
{{collaborator/usage-row|martinos}}
{{collaborator/usage-row|nac}}
{{collaborator/usage-row|namic}}
{{collaborator/usage-row|ncigt}}
{{collaborator/usage-row|nedo}}
{{collaborator/usage-row|pnl}}
{{collaborator/usage-row|python}}
{{collaborator/usage-row|sci}}
{{collaborator/usage-row|slicer4}}
{{collaborator/usage-row|sparkit}}
{{collaborator/usage-row|spl}}
{{collaborator/usage-row|uiowa}}
{{collaborator/usage-row|unc}}
{{collaborator/usage-row|upenn}}
{{collaborator/usage-end}}

[[Category:Templates|{{PAGENAME}}]]
[[Category:Templates:Logos]]
</noinclude>

Documentation/Nightly/Modules/SegmentationAidedRegistration

2013-06-08T02:39:56Z

Ygao: References