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

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<li> Face is the major diagnostic feature to identify
 
<li> Face is the major diagnostic feature to identify
<li> But brain and the CNS are affected primarily
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<li> But brain and the CNS are affected primarily([[File:Sample OPT Mouse embryo.zip]])
 
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<li> Modalities: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography ([[File:Stained registered sample mCT.zip]])
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<li> Modalities: Optical Projection Tomography([[File:OPT Crossection.PNG]]) & Micro Computed Tomography ([[File:Stained registered sample mCT.zip]])
 
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<li>We use landmarks to identify the anatomical regions across our samples which vary hugely in development.  
 
<li>We use landmarks to identify the anatomical regions across our samples which vary hugely in development.  

Revision as of 18:01, 23 June 2014

Home < 2014 Summer Project Week:Slicer Murin Shape Analysis

Key Investigators

  • Murat Maga
  • Ryan Young


Project Description

  • Research: Changes in development due to Fetal Alcohol Exposure and how this affects the development of the craniofacial complex.
  • Modalities: Optical Projection Tomography(OPT Crossection.PNG) & Micro Computed Tomography (File:Stained registered sample mCT.zip)
    • We use landmarks to identify the anatomical regions across our samples which vary hugely in development.
    • 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
    • Implement the landmark based Procrustes Analysis in Slicer


    Objective

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

    • Impliment GPA/PCA shape analysis in python
    • Visualize the deformation of a reference volume along the principle components using thin plate splines(TPS.png)
    • Ability to create semi-landmarks to increase spatial coverage.
      • 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.

    Progress

    • Generalized Procrustes Alignment
    • Principal Component and Singular Value Decomposition of the Procrustes aligned coordinates
    • 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).

    File:PowerPoint.pdf