NAMIC Wiki - User contributions [en]

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:46:30Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]] [[Image:Registered mCT scans.png|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li> Meet the community and learn from them!
<li> Raise awareness about issues in using Slicer in high-resolution small animal imaging.
<li> Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Implement GPA/PCA shape analysis in python</li>
<li>Provide an interactive tool to visualize the decomposition along the principle components of shape variation using thin plate splines.<br>
[[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase coverage in regions where anatomial landmarks are sparse. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
<li>These should be accomplished on volume, not surface meshes derived from scans.
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment (implemented)</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates (implemented)</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (implemented).</li>
<li> Transfering and sliding a template of semi-landmarks to the target volume (in progress)

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

File:Registered mCT scans.png

2014-06-24T15:45:28Z

Muratmaga:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:37:52Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li> Meet the community and learn from them!
<li> Raise awareness about issues in using Slicer in high-resolution small animal imaging.
<li> Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Implement GPA/PCA shape analysis in python</li>
<li>Provide an interactive tool to visualize the decomposition along the principle components of shape variation using thin plate splines.<br>
[[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase coverage in regions where anatomial landmarks are sparse. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
<li>These should be accomplished on volume, not surface meshes derived from scans.
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment (implemented)</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates (implemented)</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (implemented).</li>
<li> Transfering and sliding a template of semi-landmarks to the target volume (in progress)

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:36:06Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li> Meet the community and learn from them!
<li> Raise awareness about issues in using Slicer in high-resolution small animal imaging.
<li> Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Implement GPA/PCA shape analysis in python</li>
<li>Provide an interactive tool to visualize the decomposition along the principle components of shape variation using thin plate splines.<br>
[[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase coverage in regions where anatomial landmarks are sparse. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment (implemented)</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates (implemented)</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (implemented).</li>
<li> Transfering and sliding a template of semi-landmarks to the target volume (in progress)

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

File:TPS.png

2014-06-24T15:33:38Z

Muratmaga: uploaded a new version of "File:TPS.png"

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:25:28Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li> Meet the community and learn from them!
<li> Raise awareness about issues in using Slicer in high-resolution small animal imaging.
<li> Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Implement GPA/PCA shape analysis in python</li>
<li>Provide an interactive tool to visualize the decomposition along the principle components of shape variation using thin plate splines.<br>
[[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase coverage in regions where anatomial landmarks are sparse. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:23:23Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li>Meet the community and learn
<li>Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Implement GPA/PCA shape analysis in python</li>
<li>Provide an interactive tool to visualize the decomposition along the principle components of shape variation using thin plate splines.<br>
[[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:21:47Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li>Meet the community and learn
<li>Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Implement GPA/PCA shape analysis in python</li>
<li>Visualize the deformation of a reference volume along the principle components using thin plate splines([[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:20:55Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
[[Image:Fetus variation picture.PNG|400px]]
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li>Meet the community and learn
<li>Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Impliment GPA/PCA shape analysis in python</li>
<li>Visualize the deformation of a reference volume along the principle components using thin plate splines([[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

File:Fetus variation picture.PNG

2014-06-24T15:19:47Z

Muratmaga: uploaded a new version of "File:Fetus variation picture.PNG"

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:18:50Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
[[Image:OPT Crossection.PNG|100px]]
<li> Shape Analysis
<ul>
<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
([[Image:Fetus variation picture.PNG|200px]])
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li>Meet the community and learn
<li>Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Impliment GPA/PCA shape analysis in python</li>
<li>Visualize the deformation of a reference volume along the principle components using thin plate splines([[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:12:25Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> Brain and the CNS are affected primarily.
<li> What's the earliest time we begin to detect changes in the face?
<li> How does the brain volumes (and gross morphology) relate to changes in the face?
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>
<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>\
[[Image:OPT Crossection.PNG|100px]]
<ul>
<li>We use landmarks to identify the anatomical regions across our samples which vary hugely in development.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes ([[Image:Fetus variation picture.PNG|200px]]).
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li>Meet the community and learn
<li>Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Impliment GPA/PCA shape analysis in python</li>
<li>Visualize the deformation of a reference volume along the principle components using thin plate splines([[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-24T15:09:39Z

Muratmaga:

==Key Investigators==
* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
* Ryan Young (Seattle Children's Research Institute)

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<li>Research: Changes in development due to [http://www.cdc.gov/ncbddd/fasd/index.html Fetal Alcohol Exposure] and how this affects the development of the craniofacial complex.
<ul>
<li> Face is the major diagnostic feature to identify
<li> But brain and the CNS are affected primarily()
</ul>

<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]] <br>[[Image:OPT Crossection.PNG|100px]]<br>
<B> Micro Computed Tomography </b> ([[File:Stained registered sample mCT.zip]])
<ul>
<li>We use landmarks to identify the anatomical regions across our samples which vary hugely in development.
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes ([[Image:Fetus variation picture.PNG|200px]]).
</ul>

<li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. ([[File:Project week question.txt]])

<li> Goals for Project Week:
<ul>
<li>Meet the community and learn
<li>Implement the landmark based Procrustes Analysis in Slicer
</ul>

<h3>Objective</h3>
*Create a GPA/PCA shape analysis and visualization module for Slicer.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
<ul>
<li>Impliment GPA/PCA shape analysis in python</li>
<li>Visualize the deformation of a reference volume along the principle components using thin plate splines([[Image:TPS.png|400px]])</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. </b>
<ul>
<li><b>User will a uniformly sampled point cloud by entering the number of semi-landmarks. Existing “hard” landmarks will be used for their distribution. This will serve as the template to be transferred to all remaining volumes (atlas)
<li>The template will be transferred to a new surface. Existing “hard” landmarks will allow for correspondence. The transferred points will then be moved along the surface of the volume by optimizing the bending energy function.
<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
</ul>
</ul>
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
<ul>
<li> Generalized Procrustes Alignment</li>
<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (by either morphing a reference volume along the shape variable, or visualizing the TPS grid using Transformation Visualizer module).</li>

</ul>

[[File:PowerPoint.pdf|Intro Power Point]]
</div>