Difference between revisions of "2014 Summer Project Week:Slicer Murin Shape Analysis"

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==Key Investigators==
 
==Key Investigators==
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* Murat Maga
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* Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
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* Ryan Young
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* Ryan Young (Seattle Children's Research Institute)
  
  
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<div style="width: 27%; float: left; padding-right: 3%;">
 
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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.  
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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).  
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<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.
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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.
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<ul>
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<li> Face is the major diagnostic feature to identify
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<li> Brain and the CNS are affected primarily.
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<li> What's the earliest time we begin to detect changes in the face?
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<li> How does the brain volumes (and gross morphology) relate to changes in the face?
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</ul>
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<li> Modalities: <b> Optical Projection Tomography</b> [[File:Sample OPT Mouse embryo.zip]]    <br>
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<B> Micro Computed Tomography </b> [[File:Stained registered sample mCT.zip]] <br>
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[[Image:OPT Crossection.PNG|100px]] [[Image:Registered mCT scans.png|100px]]
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<li> Shape Analysis
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<ul>
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<li>We use landmarks to identify the anatomical regions across our developmental series of fetal samples.  
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<li>We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes. <br>
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[[Image:Fetus variation picture.PNG|400px]]
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</ul>
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<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]])  
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<li> Goals for Project Week:
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<ul>
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<li> Meet the community and learn from them!
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<li> Raise awareness about issues in using Slicer in high-resolution small animal imaging.
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<li> Implement the landmark based Procrustes Analysis in Slicer
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</ul>
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<h3>Objective</h3>
 
<h3>Objective</h3>
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
 
<ul>
 
<ul>
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<li>Impliment GPA/PCA shape analysis in python</li>
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<li>Implement GPA/PCA shape analysis in python</li>
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<li>Visualize the deformation of a reference volume along the principle components using thin plate splines</li>
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<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>
  
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<li>Ability to create semi-landmarks to increase spatial coverage. (Using ideas from Morpho package in R)
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<li> <b>Ability to create semi-landmarks to increase coverage in regions where anatomial landmarks are sparse. </b>
 
<ul>
 
<ul>
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<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)
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<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 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.
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<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.  
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<li>The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.</b>
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<li>These should be accomplished on volume, not surface meshes derived from scans.
 
</ul>
 
</ul>
 
</ul>  
 
</ul>  
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<h3>Progress</h3>
 
<h3>Progress</h3>
 
<ul>
 
<ul>
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<li> Generalized Procrustes Alignment</li>
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<li> Generalized Procrustes Alignment (implemented)</li>
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<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates</li>
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<li> Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates (implemented)</li>
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<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>
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<li> Thin Plate Spline visualization of the shape variables from PCA and/or SVD (implemented).</li>
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<li> Transfering and sliding a template of semi-landmarks to the target volume (in progress)
  
 
</ul>
 
</ul>
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</div>
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[[File:PowerPoint.pdf|Intro Power Point]]
 
</div>
 
</div>

Latest revision as of 15:46, 24 June 2014

Home < 2014 Summer Project Week:Slicer Murin Shape Analysis

Key Investigators

  • Murat Maga (Seattle Children's Research Institute & University of Washington Dept. of Pediatrics)
  • Ryan Young (Seattle Children's Research Institute)


Project Description

  • Research: Changes in development due to Fetal Alcohol Exposure and how this affects the development of the craniofacial complex.
    • Face is the major diagnostic feature to identify
    • Brain and the CNS are affected primarily.
    • What's the earliest time we begin to detect changes in the face?
    • How does the brain volumes (and gross morphology) relate to changes in the face?
  • Modalities: Optical Projection Tomography File:Sample OPT Mouse embryo.zip
    Micro Computed Tomography File:Stained registered sample mCT.zip
    OPT Crossection.PNG Registered mCT scans.png
  • Shape Analysis
    • We use landmarks to identify the anatomical regions across our developmental series of fetal samples.
    • We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes.
      Fetus variation picture.PNG
  • 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)
  • Goals for Project Week:
    • Meet the community and learn from them!
    • Raise awareness about issues in using Slicer in high-resolution small animal imaging.
    • Implement the landmark based Procrustes Analysis in Slicer


    Objective

    • Create a GPA/PCA shape analysis and visualization module for Slicer.

    • Approach, Plan

    • Implement GPA/PCA shape analysis in python
    • Provide an interactive tool to visualize the decomposition along the principle components of shape variation using thin plate splines.
      TPS.png)
    • Ability to create semi-landmarks to increase coverage in regions where anatomial landmarks are sparse.
      • 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)
      • 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.
      • The coordinates of the slid landmarks will be saved into a new fiducial list, from which the GPA analysis can be conducted.
      • These should be accomplished on volume, not surface meshes derived from scans.

    Progress

    • Generalized Procrustes Alignment (implemented)
    • Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates (implemented)
    • Thin Plate Spline visualization of the shape variables from PCA and/or SVD (implemented).
    • Transfering and sliding a template of semi-landmarks to the target volume (in progress)

    File:PowerPoint.pdf

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