NAMIC Wiki - User contributions [en]

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T18:02:14Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==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([[File:Sample OPT Mouse embryo.zip]])
</ul>

<li> Modalities: Optical Projection Tomography([[Image:OPT Crossection.PNG|100px]]) & Micro Computed Tomography ([[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>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T18:01:25Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==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([[File:Sample OPT Mouse embryo.zip]])
</ul>

<li> Modalities: Optical Projection Tomography([[File:OPT Crossection.PNG]]) & Micro Computed Tomography ([[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>

File:OPT Crossection.PNG

2014-06-23T18:01:07Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T17:24:43Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography ([[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>

File:TPS.png

2014-06-23T17:22:30Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T16:04:01Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography ([[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</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:Project week question.txt

2014-06-23T16:03:13Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T15:55:38Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography ([[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. (Link to the specific questions)

<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</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

2014-06-23T15:52:32Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE, [[File:http://www.cdc.gov/ncbddd/fasd/index.html]]) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography ([[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. (Link to the specific questions)

<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</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:Stained registered sample mCT.zip

2014-06-23T15:51:33Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T15:42:51Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE, [[File:http://www.cdc.gov/ncbddd/fasd/index.html]]) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography (adults)
<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. (Link to the specific questions)

<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</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

2014-06-23T15:42:37Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE, [[File:http://www.cdc.gov/ncbddd/fasd/index.html]]) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography (adults)
<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. (Link to the specific questions)

<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</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

2014-06-23T15:35:21Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography (adults)
<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. (Link to the specific questions)

<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</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

2014-06-23T15:35:01Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography (adults)
<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|100px]]).
</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. (Link to the specific questions)

<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</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

2014-06-23T15:33:35Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography (adults)
<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|Link]]).
</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. (Link to the specific questions)

<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</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

2014-06-23T15:29:22Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T15:28:35Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE) 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: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography (adults)
<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 (LINK TO FIGURE & Dataset).
</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. (Link to the specific questions)

<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</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

2014-06-23T15:23:34Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE) 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: Optical Projection Tomography (Fetuses) & Micro Computed Tomography (adults)
<ul>
<li>We use landmarks to identify the anatomical regions across our samples which vary hugely in development. (LINK TO THE FIGURE & Datsets)
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes (LINK TO FIGURE & Dataset).
</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. (Link to the specific questions)

<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</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:Sample OPT Mouse embryo.zip

2014-06-23T15:22:30Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T15:20:06Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

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

<li>Research: Changes in development due to Fetal Alcohol Exposure (FAE) 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: Optical Projection Tomography (Fetuses) & Micro Computed Tomography (adults)
<ul>
<li>We use landmarks to identify the anatomical regions across our samples which vary hugely in development. (LINK TO THE FIGURE & Datsets)
<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes (LINK TO FIGURE & Dataset).
</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. (Link to the specific links)

<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</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)</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

2014-06-23T14:48:30Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

<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</li>

<li> <b>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)</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

2014-06-23T13:40:47Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>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.
</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:PowerPoint.pdf

2014-06-23T13:30:55Z

Youngre:

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-23T12:54:59Z

Youngre: /* Images */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-05T20:00:33Z

Youngre: /* Images */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
==Images==
[[File:Label_map.png]]
Label map of a murin skull seperated from the air.
[[File:Threshold murin skull.png]]
Thresholded murin skull.
[[File:Raw data.png]]
Scan of a murin skull

</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-05T19:58:48Z

Youngre: /* Images */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
==Images==
[[File:Label_map.png]]
[[File:Threshold murin skull.png]]
[[File:Raw data.png]]

</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-05T19:57:20Z

Youngre: /* Images */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

==Images==
[[File:Label_map.png]]
[[File:Threshold murin skull.png]]
[[File:murin_skull.jpg]]

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-06-05T19:53:24Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

==Images==
[[File:Label_map.png]]
[[File:threshold_murin-skull.png]]
[[File:raw_data.png]]

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

File:Raw data.png

2014-06-05T19:51:31Z

Youngre:

File:Threshold murin skull.png

2014-06-05T19:50:00Z

Youngre:

File:Label map.png

2014-06-05T19:43:42Z

Youngre: A label map of a murnin skull. This is what we need to programically generate.

A label map of a murnin skull. This is what we need to programically generate.

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-05-28T20:24:03Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly. See [http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/403/pdf/imm403.pdf A Brief Introduction to Statistical Shape Analysis] for mathematical details.

<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-05-27T23:17:20Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly.
<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-05-27T23:15:58Z

Youngre: /* Project Description */

==Key Investigators==
* Murat Maga
* Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly.
<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

2014 Summer Project Week:Slicer Murin Shape Analysis

2014-05-27T23:14:51Z

Youngre: Created page with '==Project Description== <div style="margin: 20px;"> <div style="width: 27%; float: left; padding-right: 3%;"> Procrustes based shape analyses are a very establish set of geometr…'

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly.
<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

2014 Summer Project Week

2014-05-27T23:13:46Z

Youngre: /* Other */

__NOTOC__

[[image:PW-MIT2014.png|300px]]

Dates: June 23-27, 2014.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 23
!style="width:18%" |Tuesday, June 24
!style="width:18%" |Wednesday, June 25
!style="width:18%" |Thursday, June 26
!style="width:18%" |Friday, June 27
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT and RT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-12pm''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: DICOM|DICOM]] (Steve Pieper)
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9:30-11am: <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session:Registration Algorithms|Registration Algorithms]]'''(Sandy Wells)
[[MIT_Project_Week_Rooms#Star|Star]]
|
'''9:30-10:30am''' [[2014_Tutorial_Contest|Tutorial Contest Presentations]] <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''10am-12pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: IGT|Image-Guided Therapy - Neurosurgery]] (Alexandra Golby, Tina Kapur) <br>
[[MIT_Project_Week_Rooms#Star|Star]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]] <br>
'''12pm''' [[Events:TutorialContestJune2014|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' [[2014 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: QIICR|QIICR]] (Andrey Fedorov)
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
|'''1-2:30pm:''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: Contours|Contours]] (Adam Rankin, Csaba Pinter)
TBD [[MIT_Project_Week_Rooms#Kiva|Kiva]]
|'''1-3pm:''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: IGT|Image-Guided Therapy - Prostate Interventions]] (Clare Tempany, Tina Kapur)
<br>----------------------------------------<br>
'''3-5:30pm: <font color="#4020ff">Breakout Session:'''</font><br>TBD
[[MIT_Project_Week_Rooms#Star|Star]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Background''' ==

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

=Projects=
* [[2014_Project_Week_Template | Template for project pages]]

==TBI==

==Atrial Fibrillation==

==Huntington's Disease==

==Head and Neck Cancer==

==Slicer4 Extensions==

*[[2014_Summer_Project_Week:Multidim Data| Multidim Data]] (Kevin Wang, Andras, ?)
*[[2014_Summer_Project_Week:DICOM-SRO import| DICOM-SRO import]] (Kevin Wang)

==Cardiac==

==Stroke==

==Brain Segmentation==

==Image-Guided Therapy==

* SlicerIGT extension: testing, tutorials, website (Tamas Ungi, Junichi Tokuda)
* Gestural Point of Care Interface for IGT (Saskia, Franklin, Tobias)
* MR-Ultrasound Registration for Prostate Interventions (Chenxi Zhang, Andriy Fedorov, Andras)
* Surface approximation from contour points (Chenxi Zhang, Csaba Pinter, Andrey Fedorov)
* Steered image registration using intelligent interfaces for minimal user interaction (Marcel Prastawa, Jim Miller, Steve Pieper)

==Radiation Therapy==
*[[2014_Summer_Project_Week:External Beam Planning| External Beam Planning]] (Kevin Wang, Greg, ?)

==TMJ-OA==

==Chronic Obstructive Pulmonary Disease ==

==[http://qiicr.org QIICR]==
* Real world value mapping support (Andrey, Andras, Steve, Jim, ...)
* Segmentation object support (Andrey, Csaba, Steve, ...)

==Infrastructure==
*Chronicle (Steve Pieper)
*Volume Registration (Steve Pieper)
*OpenCL (Steve Pieper, Marcel Prastawa)
*Markups (Nicole Aucoin)
*Pluggable Label Statistics (Andrey , Ethan, Steve, Brad?, Jim? Dirk?)
*[[2014_Summer_Project_Week:Subject_hierarchy_integration | Subject hierarchy integration]] (Csaba, Steve, Jc, Andras?, ?)
*[[2014_Summer_Project_Week:Contours | Contours]] (Adam Rankin, Csaba, Andras, Steve, Jc, ?)
*[[2014_Summer_Project_Week:Parameter Node Serialization | Parameter Node Serialization]] (Kevin Wang, Andras, Steve, Jim, Matt, Csaba, ?)

==Feature Extraction==
*Breast Tumor Segmentation and Heterogeneity Analysis (Vivek Narayan, Jay Jagadeesan)
*Quantitative image feature extraction in Non-Small Cell Lung Cancer (Hugo Aerts)

==Other==
*[[2014_Summer_Project_Week:Slicer_Murin_Shape_Analysis | Shape Analysis for the developing murine skull]] (Murat Maga, Ryan Young, Seattle Chidren's Hospital).
*[[2014_Summer_Project_Week:Slicer_LDDMM_Shape_Analysis | Slicer Interface to LDDMM shape anlaysis]] (Saurabh Jain, JHU; Steve Pieper, Isomics; Josh Cates, SCI, Utah; Hans Johnson, Iowa; Martin Styner, UNC)

== '''Logistics''' ==

*'''Dates:''' June 23-27, 2014.
*'''Location:''' [[MIT_Project_Week_Rooms| Stata Center / RLE MIT]].
*'''REGISTRATION:''' https://www.regonline.com/namic2014summerprojectweek. Please note that as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on "No thanks" in order to finish your Project Week registration.
*'''Registration Fee:''' $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2014_Summer_Project_Week/RoomSharing|here]]

== '''Registrants''' ==

Do not add your name to this list - it is maintained by the organizers based on your paid registration. ([https://www.regonline.com/namic2014summerprojectweek Please click here to register.])

#Hugo Aerts, Dana Farber/Harvard, hugo_aerts@dfci.harvard.edu
#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, Brigham & Women's Hospital, nicole@bwh.harvard.edu
#Kanglin Chen, Fraunhofer MEVIS, kanglin.chen@mevis.fraunhofer.de
#Alexander Derksen, Fraunhofer MEVIS, alexander.derksen@mevis.fraunhofer.de
#Fotis Drakopoulos, Old Dominion University, fdrakopo@gmail.com
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#Hans Johnson, University of Iowa, hans-johnson@uiowa.edu
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Siqi Liu, University of Sydney, sliu4512@uni.sydney.edu.au
#Bradley Lowekamp, National Institutes of Health, blowekamp@mail.nih.gov
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's University, csaba.pinter@queensu.ca
#Adam Rankin, Queen's University, rankin@queensu.ca
#David Welch, University of Iowa, david-welch@uiowa.edu
#Paolo Zaffino, University Magna Graecia of Catanzaro, p.zaffino@unicz.it
#Fan Zhang, University of Sydney, fzha8048@uni.sydney.edu.au

2014 Project Week Template

2014-05-27T23:02:52Z

Youngre:

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

2014 Project Week Template

2014-05-27T22:49:58Z

Youngre: /* Project Description */

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
Murat Maga and
Ryan Young

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
Procrustes based shape analyses are a very establish set of geometric morphometric analysis in the realm of developmental and evolutionary biology. Although traditionally conducted on 2D pictures, with the general availability of the 3D (either volumetric or surface) data, field is moving more towards 3D analyses.
In case of 3D volumetric data, the typical workflow is to convert the scan dataset into a surface mesh by significantly reducing and smoothing, render the 3D surface on a platform capable of annotating the landmark, export the landmark coordinates into the analysis software (e.g. R), conduct the Procrustes alignment and geometric analyses, and then visualize the results using thin plate splines (TPS).
We are interested in creating a geometric morphometric analysis module within Slicer to uniform this experience. Our goal for the project week is to conduct visualization, data collection, statistical analysis and visualizations of shape variation decomposition using Slicer. Our ultimate goal is to be able to repositories (e.g. using Xnat) with already annotated specimens along with all their metadata (species, sex, age, genotype, genomic data, etc.) that can be queried within Slicer (e.g. through XnatSlicer module) and analyze them accordingly.
<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</li>

<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
<ul>
<li>User will create 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.
</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>
</div>
</div>

2014 Project Week Template

2014-05-27T22:15:04Z

Youngre: /* Key Investigators */