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

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.
  

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.
  )
• 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.

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