Difference between revisions of "2014 Summer Project Week:Slicer Murin Shape Analysis"
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| − | <li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. (Link to the specific | + | <li> Challenges in Slicer with our datasets due to small voxel sizes (6-35 micron). Specifically visualization, recording coordinates of anatomical landmarks, segmentation and registration. (Link to the specific questions) |
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<li>Visualize the deformation of a reference volume along the principle components using thin plate splines</li> | <li>Visualize the deformation of a reference volume along the principle components using thin plate splines</li> | ||
| − | <li> <b>Ability to create semi-landmarks to increase spatial coverage. | + | <li> <b>Ability to create semi-landmarks to increase spatial coverage. </b> |
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<li><b>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) | <li><b>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) | ||
Revision as of 15:23, 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
- We use landmarks to identify the anatomical regions across our samples which vary hugely in development. (LINK TO THE FIGURE & Datsets)
- We want to be able segment brains from about 600 volumes and do a coupled analysis of facial and brain phenotypes (LINK TO FIGURE & Dataset).
- 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).
