Difference between revisions of "2014 Summer Project Week:Slicer Murin Shape Analysis"
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− | <li> Modalities: Optical Projection Tomography([[File:Sample OPT Mouse embryo.zip]], Fetuses) & Micro Computed Tomography ( | + | <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>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 15:52, 23 June 2014
Home < 2014 Summer Project Week:Slicer Murin Shape AnalysisKey Investigators
- Murat Maga
- Ryan Young
Project Description
- Face is the major diagnostic feature to identify
- But brain and the CNS are affected primarily
- 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).