2014 Summer Project Week:Slicer Murin Shape Analysis

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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


    • 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.
    • 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.


    • 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)