Slicer Wiki - User contributions [en]

Documentation/Nightly/Extensions/SegmentationWizard

2017-07-28T14:24:10Z

Abeers:

<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}}

{|
[[File:3D Model Segmentation.png]]
|
Extension: [[Documentation/{{documentation/version}}/Extensions/SegmentationWizard|SegmentationWizard]] 
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative Image Informatics for Cancer Research (QIICR) (U24CA180918)
* Quantitative MRI of Glioblastoma Response (U01CA154601)
* Informatics Tools for Optimized Imaging Biomarkers for Cancer Research & Discovery (U24CA180927) 
Authors: Andrew Beers (Massachusetts General Hospital) 
Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> 

License: [http://www.slicer.org/pages/LicenseText Slicer License]
|}

{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|qiicr}}|{{collaborator|longname|qiicr}}
}}

{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}

{|
The SegmentationWizard module allows users to quickly create smooth, 3D regions of interest with only a few clicks, and then provides a variety of tools to threshold and refine those regions of interest. The module is broken up into several steps, which the user can progress through with "Back" and "Next" buttons on the bottom of the module panel. The module can be used on single volumes, or on pre- and post-contrast pairs. In the latter case, utilities for registering, normalizing, and subtracting volumes are provided. Check the tutorials section for some usage examples, and send any bugs, comments, or feature requests to abeers@mgh.harvard.edu.
 
It is our intention to add semi-automatic and automatic segmentation tools based on deep learning and other machine learning methods to this module in the future. Check back in sometime soon!

 
|}


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Tissue segmentation with and pre- and post-contrast images.
* Intensity thresholding for parametric maps.
* Subtraction map creation and registration.
* Adaptive volume cropping


{{documentation/{{documentation/version}}/module-section|Tutorials}}
https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s


{{documentation/{{documentation/version}}/module-section|Gallery}}
<gallery>
SegmentationWizardGallery1.PNG|Making a model
SegmentationWizardGallery2.PNG|Reviewing a ROI
SegmentationWizardGallery3.PNG|Creating a subtraction map
SegmentationWizardGallery4.PNG|Thresholding an ROI
SegmentationWizardGallery5.PNG|Creating a Lung CT ROI
SegmentationWizardGallery6.PNG|Thresholding a Lung CT ROI
</gallery>


{{documentation/{{documentation/version}}/module-section|Documentation}}
{|
|
== Step 1. Volume Selection ==
{|
|<gallery>
3D_Model_Segmentation_Step_1.PNG|Step 1 User Interface
</gallery>
|
*'''Volume Selection''': Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped.
|}
== Step 2. Registration (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_2.PNG|Step 2 User Interface
</gallery>
|
*'''Registration Order''': Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm.
*'''Registration Method''': Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and create a BSpline Transform object. In general, each method will be more likely to be accurate but also more time-consuming than the previous method.
*'''Registration Output''': Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]".
|}
== Step 3. Normalization and Subtraction (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_3A.PNG|Step 3 User Interface
3D_Model_Segmentation_Step_3B.PNG|Step 3 Example Subtraction Map
</gallery>
|
*'''Normalization Methods''': Choose the intensity normalization method for your two volumes. This may help to produce better contrast when calculating a subtraction map.
*'''Normalization Order''': Choose whether to normalize pre- to post-contrast, or vice versa.
*'''Calculate Subtraction Map''': Subtract Volume 1 from Volume 2 using Slicer's "Subtract Scalar Volumes" module. The output subtraction volume will be the basis for segmentation in future steps. The output volume's name will be displayed as "[VolumeName2]_subtraction".
|}
== Step 4. ROI ==
{|
|<gallery>
3D_Model_Segmentation_Step_4A.PNG|Step 4 User Interface
3D_Model_Segmentation_Step_4B.PNG|Step 4 Example ROI from Markups
</gallery>
|
*'''ROI Toolbar''': Use the ROI Toolbar to create a smooth, convex segmentation around your region of interest. Use the Model Marker Placement Tool to lay down border points. Once you place more than three border points, a best-guess segmentation will automatically be created from your border points. You can add as many border points as needed, with the segmentation guess automatically adjusting as necessary. You can move existing border points by de-selecting the Model Marker Placement Tool, and clicking and dragging using the basic mouse tool. The model will update accordingly.
*'''Model-Based ROI''': Use this drop-down menu to switch between different sets of markup points. Additional points added with the ROI toolbar will update whichever model is selected in this drop-down menu. Pre-existing sets of points can also be loaded into this menu and turned into the template for a segmentation.
|}
== Step 5. Thresholding ==
{|
|<gallery>
3D_Model_Segmentation_Step_5A.PNG|Step 5 User Interface
3D_Model_Segmentation_Step_5B.PNG|Step 5 Example Thresholded ROI
</gallery>
|
*'''Threshold Range''': Adjust this slider to set a simple intensity threshold on the smooth segmentation created in the previous step. Voxels in the newly-created label named "[VolumeName1]_roi_label_thresholded" with intensities outside of the slider's bounds will be erased. The original segmentation without any thresholding is available under a second label named "[VolumeName1]_roi_label", and a version of the original volume cropped to that label is available under the name "[VolumeName1]_roi_cropped".
|}
== Step 6. Review ==
{|
|<gallery>
3D_Model_Segmentation_Step_6.PNG|Step 6 User Interface
</gallery>
|
*'''Editor Tools''': Use these tools from Slicer's Editor module to clean up your segmentation. You can also switch to the Editor or Segmentations modules for more label editing options.
*'''Restart''': Remove segmentation objects you no longer need, and go back to Step 1. While you can go back to previous steps via the "Back" buttons, it is recommended that you use the Restart button in this step when segmenting an entirely new volume. This will reset all of the steps back to their original state.
|}



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
Volume Clip - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/VolumeClip 
ChangeTracker - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/ChangeTracker


{{documentation/{{documentation/version}}/module-section|References}}
None


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source Code: https://github.com/QTIM-Lab/SlicerSegmentationWizard


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

Documentation/Nightly/Extensions/SegmentationWizard

2017-07-27T17:58:36Z

Abeers:

<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}}

{|
[[File:3D Model Segmentation.png]]
|
Extension: [[Documentation/{{documentation/version}}/Extensions/SegmentationWizard|SegmentationWizard]] 
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative Image Informatics for Cancer Research (QIICR) (U24CA180918)
* Quantitative MRI of Glioblastoma Response (U01CA154601)
* Informatics Tools for Optimized Imaging Biomarkers for Cancer Research & Discovery (U24CA180927) 
Authors: Andrew Beers (Massachusetts General Hospital) 
Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> 

License: [http://www.slicer.org/pages/LicenseText Slicer License]
|}

{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|qiicr}}|{{collaborator|longname|qiicr}}
}}

{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}

{|
The SegmentationWizard module allows users to quickly create smooth, 3D regions of interest with only a few clicks, and then provides a variety of tools to threshold and refine those regions of interest. The module is broken up into several steps, which the user can progress through with "Back" and "Next" buttons on the bottom of the module panel. The module can be used on single volumes, or on pre- and post-contrast pairs. In the latter case, utilities for registering, normalizing, and subtracting volumes are provided. Check the tutorials section for some usage examples, and send any bugs, comments, or feature requests to abeers@mgh.harvard.edu.
 
It is our intention to add semi-automatic and automatic segmentation tools based on deep learning and other machine learning methods to this module in the future. Check back in sometime soon!

 
|}


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Tissue segmentation with and pre- and post-contrast images.
* Intensity thresholding for parametric maps.
* Subtraction map creation and registration.
* Adaptive volume cropping


{{documentation/{{documentation/version}}/module-section|Tutorials}}
https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s


{{documentation/{{documentation/version}}/module-section|Gallery}}
<gallery>
SegmentationWizardGallery1.PNG|Making a model
SegmentationWizardGallery2.PNG|Reviewing a ROI
SegmentationWizardGallery3.PNG|Creating a subtraction map
SegmentationWizardGallery4.PNG|Thresholding an ROI
SegmentationWizardGallery5.PNG|Creating a Lung CT ROI
SegmentationWizardGallery6.PNG|Thresholding a Lung CT ROI
</gallery>


{{documentation/{{documentation/version}}/module-section|Documentation}}
{|
|
== Step 1. Volume Selection ==
{|
|<gallery>
3D_Model_Segmentation_Step_1.PNG|Step 1 User Interface
</gallery>
|
*'''Volume Selection''': Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped.
|}
== Step 2. Registration (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_2.PNG|Step 2 User Interface
</gallery>
|
*'''Registration Order''': Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm.
*'''Registration Method''': Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and create a BSpline Transform object. In general, each method will be more likely to be accurate but also more time-consuming than the previous method.
*'''Registration Output''': Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]".
|}
== Step 3. Normalization and Subtraction (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_3A.PNG|Step 3 User Interface
3D_Model_Segmentation_Step_3B.PNG|Step 3 Example Subtraction Map
</gallery>
|
*'''Normalization Methods''': Choose the intensity normalization method for your two volumes. This may help to produce better contrast when calculating a subtraction map.
*'''Normalization Order''': Choose whether to normalize pre- to post-contrast, or vice versa.
*'''Calculate Subtraction Map''': Subtract Volume 1 from Volume 2 using Slicer's "Subtract Scalar Volumes" module. The output subtraction volume will be the basis for segmentation in future steps. The output volume's name will be displayed as "[VolumeName2]_subtraction".
|}
== Step 4. ROI ==
{|
|<gallery>
3D_Model_Segmentation_Step_4A.PNG|Step 4 User Interface
3D_Model_Segmentation_Step_4B.PNG|Step 4 Example ROI from Markups
</gallery>
|
*'''ROI Toolbar''': Use the ROI Toolbar to create a smooth, convex segmentation around your region of interest. Use the Model Marker Placement Tool to lay down border points. Once you place more than three border points, a best-guess segmentation will automatically be created from your border points. You can add as many border points as needed, with the segmentation guess automatically adjusting as necessary. You can move existing border points by de-selecting the Model Marker Placement Tool, and clicking and dragging using the basic mouse tool. The model will update accordingly.
*'''Model-Based ROI''': Use this drop-down menu to switch between different sets of markup points. Additional points added with the ROI toolbar will update whichever model is selected in this drop-down menu. Pre-existing sets of points can also be loaded into this menu and turned into the template for a segmentation.
|}
== Step 5. Thresholding ==
{|
|<gallery>
3D_Model_Segmentation_Step_5A.PNG|Step 5 User Interface
3D_Model_Segmentation_Step_5B.PNG|Step 5 Example Thresholded ROI
</gallery>
|
*'''Threshold Range''': Adjust this slider to set a simple intensity threshold on the smooth segmentation created in the previous step. Voxels in the newly-created label named "[VolumeName1]_roi_label_thresholded" with intensities outside of the slider's bounds will be erased. The original segmentation without any thresholding is available under a second label named "[VolumeName1]_roi_label", and a version of the original volume cropped to that label is available under the name "[VolumeName1]_roi_cropped".
|}
== Step 6. Review ==
{|
|<gallery>
3D_Model_Segmentation_Step_6.PNG|Step 6 User Interface
</gallery>
|
*'''Editor Tools''': Use these tools from Slicer's Editor module to clean up your segmentation. You can also switch to the Editor or Segmentations modules for more label editing options.
*'''Restart''': Remove segmentation objects you no longer need, and go back to Step 1. While you can go back to previous steps via the "Back" buttons, it is recommended that you use the Restart button in this step when segmenting an entirely new volume. This will reset all of the steps back to their original state.
|}



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
Volume Clip - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/VolumeClip 
ChangeTracker - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/ChangeTracker


{{documentation/{{documentation/version}}/module-section|References}}
None


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source Code: https://github.com/QTIM-Lab/3D_Model_Segmentation


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

File:SegmentationWizardGallery6.PNG

2017-07-27T17:55:31Z

Abeers:

File:SegmentationWizardGallery5.PNG

2017-07-27T17:55:18Z

Abeers:

File:SegmentationWizardGallery4.PNG

2017-07-27T17:55:10Z

Abeers:

File:SegmentationWizardGallery3.PNG

2017-07-27T17:54:58Z

Abeers:

File:SegmentationWizardGallery2.PNG

2017-07-27T17:54:49Z

Abeers:

File:SegmentationWizardGallery1.PNG

2017-07-27T17:54:36Z

Abeers:

Documentation/Nightly/Extensions/SegmentationWizard

2017-07-27T16:58:41Z

Abeers: Created page with "<noinclude>{{documentation/versioncheck}}</noinclude>  {{documentation/{{documentation/version}}/module-header}} <!-- --------------------..."

<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}}

{|
[[File:3D Model Segmentation.png]]
|
Extension: [[Documentation/{{documentation/version}}/Extensions/SegmentationWizard|SegmentationWizard]] 
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative Image Informatics for Cancer Research (QIICR) (U24CA180918)
* Quantitative MRI of Glioblastoma Response (U01CA154601)
* Informatics Tools for Optimized Imaging Biomarkers for Cancer Research & Discovery (U24CA180927) 
Authors: Andrew Beers (Massachusetts General Hospital) 
Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> 

License: [http://www.slicer.org/pages/LicenseText Slicer License]
|}

{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|qiicr}}|{{collaborator|longname|qiicr}}
}}

{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}

{|
The SegmentationWizard module allows users to quickly create smooth, 3D regions of interest with only a few clicks, and then provides a variety of tools to threshold and refine those regions of interest. The module is broken up into several steps, which the user can progress through with "Back" and "Next" buttons on the bottom of the module panel. The module can be used on single volumes, or on pre- and post-contrast pairs. In the latter case, utilities for registering, normalizing, and subtracting volumes are provided. Check the tutorials section for some usage examples, and send any bugs, comments, or feature requests to abeers@mgh.harvard.edu.
 
It is our intention to add semi-automatic and automatic segmentation tools based on deep learning and other machine learning methods to this module in the future. Check back in sometime soon!

 
|}


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Tissue segmentation with and pre- and post-contrast images.
* Intensity thresholding for parametric maps.
* Subtraction map creation and registration.
* Adaptive volume cropping


{{documentation/{{documentation/version}}/module-section|Tutorials}}
https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s


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


{{documentation/{{documentation/version}}/module-section|Documentation}}
{|
|
== Step 1. Volume Selection ==
{|
|<gallery>
3D_Model_Segmentation_Step_1.PNG|Step 1 User Interface
</gallery>
|
*'''Volume Selection''': Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped.
|}
== Step 2. Registration (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_2.PNG|Step 2 User Interface
</gallery>
|
*'''Registration Order''': Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm.
*'''Registration Method''': Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and create a BSpline Transform object. In general, each method will be more likely to be accurate but also more time-consuming than the previous method.
*'''Registration Output''': Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]".
|}
== Step 3. Normalization and Subtraction (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_3A.PNG|Step 3 User Interface
3D_Model_Segmentation_Step_3B.PNG|Step 3 Example Subtraction Map
</gallery>
|
*'''Normalization Methods''': Choose the intensity normalization method for your two volumes. This may help to produce better contrast when calculating a subtraction map.
*'''Normalization Order''': Choose whether to normalize pre- to post-contrast, or vice versa.
*'''Calculate Subtraction Map''': Subtract Volume 1 from Volume 2 using Slicer's "Subtract Scalar Volumes" module. The output subtraction volume will be the basis for segmentation in future steps. The output volume's name will be displayed as "[VolumeName2]_subtraction".
|}
== Step 4. ROI ==
{|
|<gallery>
3D_Model_Segmentation_Step_4A.PNG|Step 4 User Interface
3D_Model_Segmentation_Step_4B.PNG|Step 4 Example ROI from Markups
</gallery>
|
*'''ROI Toolbar''': Use the ROI Toolbar to create a smooth, convex segmentation around your region of interest. Use the Model Marker Placement Tool to lay down border points. Once you place more than three border points, a best-guess segmentation will automatically be created from your border points. You can add as many border points as needed, with the segmentation guess automatically adjusting as necessary. You can move existing border points by de-selecting the Model Marker Placement Tool, and clicking and dragging using the basic mouse tool. The model will update accordingly.
*'''Model-Based ROI''': Use this drop-down menu to switch between different sets of markup points. Additional points added with the ROI toolbar will update whichever model is selected in this drop-down menu. Pre-existing sets of points can also be loaded into this menu and turned into the template for a segmentation.
|}
== Step 5. Thresholding ==
{|
|<gallery>
3D_Model_Segmentation_Step_5A.PNG|Step 5 User Interface
3D_Model_Segmentation_Step_5B.PNG|Step 5 Example Thresholded ROI
</gallery>
|
*'''Threshold Range''': Adjust this slider to set a simple intensity threshold on the smooth segmentation created in the previous step. Voxels in the newly-created label named "[VolumeName1]_roi_label_thresholded" with intensities outside of the slider's bounds will be erased. The original segmentation without any thresholding is available under a second label named "[VolumeName1]_roi_label", and a version of the original volume cropped to that label is available under the name "[VolumeName1]_roi_cropped".
|}
== Step 6. Review ==
{|
|<gallery>
3D_Model_Segmentation_Step_6.PNG|Step 6 User Interface
</gallery>
|
*'''Editor Tools''': Use these tools from Slicer's Editor module to clean up your segmentation. You can also switch to the Editor or Segmentations modules for more label editing options.
*'''Restart''': Remove segmentation objects you no longer need, and go back to Step 1. While you can go back to previous steps via the "Back" buttons, it is recommended that you use the Restart button in this step when segmenting an entirely new volume. This will reset all of the steps back to their original state.
|}



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
Volume Clip - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/VolumeClip 
ChangeTracker - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/ChangeTracker


{{documentation/{{documentation/version}}/module-section|References}}
None


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source Code: https://github.com/QTIM-Lab/3D_Model_Segmentation


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

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-20T22:09:25Z

Abeers: Grant updates

<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}}

{|
[[File:3D Model Segmentation.png]]
|
Extension: [[Documentation/{{documentation/version}}/Extensions/3D_Model_Segmentation|3D_Model_Segmentation]] 
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative Image Informatics for Cancer Research (QIICR) (U24CA180918)
* Quantitative MRI of Glioblastoma Response (U01CA154601)
* Informatics Tools for Optimized Imaging Biomarkers for Cancer Research & Discovery (U24CA180927) 
Authors: Andrew Beers (Massachusetts General Hospital) 
Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> 

License: [http://www.slicer.org/pages/LicenseText Slicer License]
|}

{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|qiicr}}|{{collaborator|longname|qiicr}}
}}

{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}

{|
The 3D Model Segmentation module allows users to quickly create smooth, 3D regions of interest with only a few clicks, and then provides a variety of tools to threshold and refine those regions of interest. The module is broken up into several steps, which the user can progress through with "Back" and "Next" buttons on the bottom of the module panel. The module can be used on single volumes, or on pre- and post-contrast pairs. In the latter case, utilities for registering, normalizing, and subtracting volumes are provided. Check the tutorials section for some usage examples, and send any bugs, comments, or feature requests to abeers@mgh.harvard.edu.
 
It is our intention to add semi-automatic and automatic segmentation tools based on deep learning and other machine learning methods to this module in the future. Check back in sometime soon!

 
|}


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Tissue segmentation with and pre- and post-contrast images.
* Intensity thresholding for parametric maps.
* Subtraction map creation and registration.
* Adaptive volume cropping


{{documentation/{{documentation/version}}/module-section|Tutorials}}
https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s


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


{{documentation/{{documentation/version}}/module-section|Documentation}}
{|
|
== Step 1. Volume Selection ==
{|
|<gallery>
3D_Model_Segmentation_Step_1.PNG|Step 1 User Interface
</gallery>
|
*'''Volume Selection''': Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped.
|}
== Step 2. Registration (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_2.PNG|Step 2 User Interface
</gallery>
|
*'''Registration Order''': Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm.
*'''Registration Method''': Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and create a BSpline Transform object. In general, each method will be more likely to be accurate but also more time-consuming than the previous method.
*'''Registration Output''': Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]".
|}
== Step 3. Normalization and Subtraction (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_3A.PNG|Step 3 User Interface
3D_Model_Segmentation_Step_3B.PNG|Step 3 Example Subtraction Map
</gallery>
|
*'''Normalization Methods''': Choose the intensity normalization method for your two volumes. This may help to produce better contrast when calculating a subtraction map.
*'''Normalization Order''': Choose whether to normalize pre- to post-contrast, or vice versa.
*'''Calculate Subtraction Map''': Subtract Volume 1 from Volume 2 using Slicer's "Subtract Scalar Volumes" module. The output subtraction volume will be the basis for segmentation in future steps. The output volume's name will be displayed as "[VolumeName2]_subtraction".
|}
== Step 4. ROI ==
{|
|<gallery>
3D_Model_Segmentation_Step_4A.PNG|Step 4 User Interface
3D_Model_Segmentation_Step_4B.PNG|Step 4 Example ROI from Markups
</gallery>
|
*'''ROI Toolbar''': Use the ROI Toolbar to create a smooth, convex segmentation around your region of interest. Use the Model Marker Placement Tool to lay down border points. Once you place more than three border points, a best-guess segmentation will automatically be created from your border points. You can add as many border points as needed, with the segmentation guess automatically adjusting as necessary. You can move existing border points by de-selecting the Model Marker Placement Tool, and clicking and dragging using the basic mouse tool. The model will update accordingly.
*'''Model-Based ROI''': Use this drop-down menu to switch between different sets of markup points. Additional points added with the ROI toolbar will update whichever model is selected in this drop-down menu. Pre-existing sets of points can also be loaded into this menu and turned into the template for a segmentation.
|}
== Step 5. Thresholding ==
{|
|<gallery>
3D_Model_Segmentation_Step_5A.PNG|Step 5 User Interface
3D_Model_Segmentation_Step_5B.PNG|Step 5 Example Thresholded ROI
</gallery>
|
*'''Threshold Range''': Adjust this slider to set a simple intensity threshold on the smooth segmentation created in the previous step. Voxels in the newly-created label named "[VolumeName1]_roi_label_thresholded" with intensities outside of the slider's bounds will be erased. The original segmentation without any thresholding is available under a second label named "[VolumeName1]_roi_label", and a version of the original volume cropped to that label is available under the name "[VolumeName1]_roi_cropped".
|}
== Step 6. Review ==
{|
|<gallery>
3D_Model_Segmentation_Step_6.PNG|Step 6 User Interface
</gallery>
|
*'''Editor Tools''': Use these tools from Slicer's Editor module to clean up your segmentation. You can also switch to the Editor or Segmentations modules for more label editing options.
*'''Restart''': Remove segmentation objects you no longer need, and go back to Step 1. While you can go back to previous steps via the "Back" buttons, it is recommended that you use the Restart button in this step when segmenting an entirely new volume. This will reset all of the steps back to their original state.
|}



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
Volume Clip - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/VolumeClip 
ChangeTracker - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/ChangeTracker


{{documentation/{{documentation/version}}/module-section|References}}
None


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source Code: https://github.com/QTIM-Lab/3D_Model_Segmentation


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

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-19T18:43:06Z

Abeers: Reformatting

<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}}

{|
[[File:3D Model Segmentation.png]]
|
Extension: [[Documentation/{{documentation/version}}/Extensions/3D_Model_Segmentation|3D_Model_Segmentation]] 
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative MRI [...] 
Authors: Andrew Beers (Massachusetts General Hospital) 
Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> 

License: [http://www.slicer.org/pages/LicenseText Slicer License]
|}

{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|qiicr}}|{{collaborator|longname|qiicr}}
}}

{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}

{|
The 3D Model Segmentation module allows users to quickly create smooth, 3D regions of interest with only a few clicks, and then provides a variety of tools to threshold and refine those regions of interest. The module is broken up into several steps, which the user can progress through with "Back" and "Next" buttons on the bottom of the module panel. The module can be used on single volumes, or on pre- and post-contrast pairs. In the latter case, utilities for registering, normalizing, and subtracting volumes are provided. Check the tutorials section for some usage examples, and send any bugs, comments, or feature requests to abeers@mgh.harvard.edu.
 
It is our intention to add semi-automatic and automatic segmentation tools based on deep learning and other machine learning methods to this module in the future. Check back in sometime soon!

 
|}


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Tissue segmentation with and pre- and post-contrast images.
* Intensity thresholding for parametric maps.
* Subtraction map creation and registration.
* Adaptive volume cropping


{{documentation/{{documentation/version}}/module-section|Tutorials}}
https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s


{{documentation/{{documentation/version}}/module-section|Documentation}}
{|
|
== Step 1. Volume Selection ==
{|
|<gallery>
3D_Model_Segmentation_Step_1.PNG|Step 1 User Interface
</gallery>
|
*'''Volume Selection''': Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped.
|}
== Step 2. Registration (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_2.PNG|Step 2 User Interface
</gallery>
|
*'''Registration Order''': Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm.
*'''Registration Method''': Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and create a BSpline Transform object. In general, each method will be more likely to be accurate but also more time-consuming than the previous method.
*'''Registration Output''': Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]".
|}
== Step 3. Normalization and Subtraction (Optional) ==
{|
|<gallery>
3D_Model_Segmentation_Step_3A.PNG|Step 3 User Interface
3D_Model_Segmentation_Step_3B.PNG|Step 3 Example Subtraction Map
</gallery>
|
*'''Normalization Methods''': Choose the intensity normalization method for your two volumes. This may help to produce better contrast when calculating a subtraction map.
*'''Normalization Order''': Choose whether to normalize pre- to post-contrast, or vice versa.
*'''Calculate Subtraction Map''': Subtract Volume 1 from Volume 2 using Slicer's "Subtract Scalar Volumes" module. The output subtraction volume will be the basis for segmentation in future steps. The output volume's name will be displayed as "[VolumeName2]_subtraction".
|}
== Step 4. ROI ==
{|
|<gallery>
3D_Model_Segmentation_Step_4A.PNG|Step 4 User Interface
3D_Model_Segmentation_Step_4B.PNG|Step 4 Example ROI from Markups
</gallery>
|
*'''ROI Toolbar''': Use the ROI Toolbar to create a smooth, convex segmentation around your region of interest. Use the Model Marker Placement Tool to lay down border points. Once you place more than three border points, a best-guess segmentation will automatically be created from your border points. You can add as many border points as needed, with the segmentation guess automatically adjusting as necessary. You can move existing border points by de-selecting the Model Marker Placement Tool, and clicking and dragging using the basic mouse tool. The model will update accordingly.
*'''Model-Based ROI''': Use this drop-down menu to switch between different sets of markup points. Additional points added with the ROI toolbar will update whichever model is selected in this drop-down menu. Pre-existing sets of points can also be loaded into this menu and turned into the template for a segmentation.
|}
== Step 5. Thresholding ==
{|
|<gallery>
3D_Model_Segmentation_Step_5A.PNG|Step 5 User Interface
3D_Model_Segmentation_Step_5B.PNG|Step 5 Example Thresholded ROI
</gallery>
|
*'''Threshold Range''': Adjust this slider to set a simple intensity threshold on the smooth segmentation created in the previous step. Voxels in the newly-created label named "[VolumeName1]_roi_label_thresholded" with intensities outside of the slider's bounds will be erased. The original segmentation without any thresholding is available under a second label named "[VolumeName1]_roi_label", and a version of the original volume cropped to that label is available under the name "[VolumeName1]_roi_cropped".
|}
== Step 6. Review ==
{|
|<gallery>
3D_Model_Segmentation_Step_6.PNG|Step 6 User Interface
</gallery>
|
*'''Editor Tools''': Use these tools from Slicer's Editor module to clean up your segmentation. You can also switch to the Editor or Segmentations modules for more label editing options.
*'''Restart''': Remove segmentation objects you no longer need, and go back to Step 1. While you can go back to previous steps via the "Back" buttons, it is recommended that you use the Restart button in this step when segmenting an entirely new volume. This will reset all of the steps back to their original state.
|}



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
Volume Clip - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/VolumeClip 
ChangeTracker - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/ChangeTracker


{{documentation/{{documentation/version}}/module-section|References}}
None


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source Code: https://github.com/QTIM-Lab/3D_Model_Segmentation


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

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-19T15:16:03Z

Abeers: First Draft of Documentation page

<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}}

{|
[[File:3D Model Segmentation.png]]
|
Extension: [[Documentation/{{documentation/version}}/Extensions/3D_Model_Segmentation|3D_Model_Segmentation]] 
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative MRI [...] 
Authors: Andrew Beers (Massachusetts General Hospital) 
Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> 

License: [http://www.slicer.org/pages/LicenseText Slicer License]
|}

{{documentation/{{documentation/version}}/module-introduction-logo-gallery
|{{collaborator|logo|qiicr}}|{{collaborator|longname|qiicr}}
}}

{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}

{|
The 3D Model Segmentation module allows users to quickly create smooth, 3D regions of interest with only a few clicks, and then provides a variety of tools to threshold and refine those regions of interest. The module is broken up into several steps, which the user can progress through with "Back" and "Next" buttons on the bottom of the module panel. The module can be used on single volumes, or on pre- and post-contrast pairs. In the latter case, utilities for registering, normalizing, and subtracting volumes are provided. Check the tutorials section for some usage examples, and send any bugs, comments, or feature requests to abeers@mgh.harvard.edu.
 
It is our intention to add semi-automatic and automatic segmentation tools based on deep learning and other machine learning methods to this module in the future. Check back in sometime soon!

 
|}


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Tissue segmentation with and pre- and post-contrast images.
* Intensity thresholding for parametric maps.
* Subtraction map creation and registration.
* Adaptive volume cropping


{{documentation/{{documentation/version}}/module-section|Tutorials}}
https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s


{{documentation/{{documentation/version}}/module-section|Documentation}}
{|
|
== Step 1. Volume Selection ==
*'''Volume Selection''': Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped.
<gallery>
3D_Model_Segmentation_Step_1.PNG|Step 1 User Interface
</gallery>
== Step 2. Registration (Optional) ==
*'''Registration Order''': Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm.
*'''Registration Method''': Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and create a BSpline Transform object. In general, each method will be more likely to be accurate but also more time-consuming than the previous method.
*'''Registration Output''': Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]".
<gallery>
3D_Model_Segmentation_Step_2.PNG|Step 2 User Interface
</gallery>
== Step 3. Normalization and Subtraction (Optional) ==
*'''Normalization Methods''': Choose the intensity normalization method for your two volumes. This may help to produce better contrast when calculating a subtraction map.
*'''Normalization Order''': Choose whether to normalize pre- to post-contrast, or vice versa.
*'''Calculate Subtraction Map''': Subtract Volume 1 from Volume 2 using Slicer's "Subtract Scalar Volumes" module. The output subtraction volume will be the basis for segmentation in future steps. The output volume's name will be displayed as "[VolumeName2]_subtraction".
<gallery>
3D_Model_Segmentation_Step_3A.PNG|Step 3 User Interface
3D_Model_Segmentation_Step_3B.PNG|Step 3 Example Subtraction Map
</gallery>
== Step 4. ROI ==
*'''ROI Toolbar''': Use the ROI Toolbar to create a smooth, convex segmentation around your region of interest. Use the Model Marker Placement Tool to lay down border points. Once you place more than three border points, a best-guess segmentation will automatically be created from your border points. You can add as many border points as needed, with the segmentation guess automatically adjusting as necessary. You can move existing border points by de-selecting the Model Marker Placement Tool, and clicking and dragging using the basic mouse tool. The model will update accordingly.
*'''Model-Based ROI''': Use this drop-down menu to switch between different sets of markup points. Additional points added with the ROI toolbar will update whichever model is selected in this drop-down menu. Pre-existing sets of points can also be loaded into this menu and turned into the template for a segmentation.
<gallery>
3D_Model_Segmentation_Step_4A.PNG|Step 4 User Interface
3D_Model_Segmentation_Step_4B.PNG|Step 4 Example ROI from Markups
</gallery>
== Step 5. Thresholding ==
*'''Threshold Range''': Adjust this slider to set a simple intensity threshold on the smooth segmentation created in the previous step. Voxels in the newly-created label named "[VolumeName1]_roi_label_thresholded" with intensities outside of the slider's bounds will be erased. The original segmentation without any thresholding is available under a second label named "[VolumeName1]_roi_label", and a version of the original volume cropped to that label is available under the name "[VolumeName1]_roi_cropped".
<gallery>
3D_Model_Segmentation_Step_5A.PNG|Step 5 User Interface
3D_Model_Segmentation_Step_5B.PNG|Step 5 Example Thresholded ROI
</gallery>
== Step 6. Review ==
*'''Editor Tools''': Use these tools from Slicer's Editor module to clean up your segmentation. You can also switch to the Editor or Segmentations modules for more label editing options.
*'''Restart''': Remove segmentation objects you no longer need, and go back to Step 1. While you can go back to previous steps via the "Back" buttons, it is recommended that you use the Restart button in this step when segmenting an entirely new volume. This will reset all of the steps back to their original state.
<gallery>
3D_Model_Segmentation_Step_6.PNG|Step 6 User Interface
</gallery>



{{documentation/{{documentation/version}}/module-section|Similar Modules}}
Volume Clip - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/VolumeClip 
ChangeTracker - https://www.slicer.org/wiki/Documentation/Nightly/Extensions/ChangeTracker


{{documentation/{{documentation/version}}/module-section|References}}
None


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source Code: https://github.com/QTIM-Lab/3D_Model_Segmentation


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

File:3D Model Segmentation Step 6.PNG

2017-07-19T15:12:49Z

Abeers:

File:3D Model Segmentation Step 5B.PNG

2017-07-19T15:12:42Z

Abeers:

File:3D Model Segmentation Step 5A.PNG

2017-07-19T15:12:35Z

Abeers:

File:3D Model Segmentation Step 4B.PNG

2017-07-19T15:12:28Z

Abeers:

File:3D Model Segmentation Step 4A.PNG

2017-07-19T15:12:21Z

Abeers:

File:3D Model Segmentation Step 3B.PNG

2017-07-19T15:12:07Z

Abeers:

File:3D Model Segmentation Step 3A.PNG

2017-07-19T15:11:57Z

Abeers:

File:3D Model Segmentation Step 2.PNG

2017-07-17T21:31:50Z

Abeers:

File:3D Model Segmentation Step 1.PNG

2017-07-17T21:29:17Z

Abeers: For 3d Model Segmentation documentation.

For 3d Model Segmentation documentation.

File:3D Model Segmentation.png

2017-07-17T19:16:09Z

Abeers: Icon for 3D_Model_Segmentation Module.

Icon for 3D_Model_Segmentation Module.

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-11T04:11:39Z

Abeers: Draft edits.

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-10T22:36:28Z

Abeers: Draft edits

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-10T15:27:25Z

Abeers: Brief doc edits

Documentation/Nightly/Extensions/3D Model Segmentation

2017-07-10T15:24:20Z

Abeers: Creating a template page for the new 3D Model Segmentation extension.