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
• But brain and the CNS are affected primarily()

Modalities: Optical Projection Tomography File:Sample OPT Mouse embryo.zip

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 ().

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()
• 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