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CCB Local Shape Analysis Workflow
Overview - Local Shape Analysis (LSA) Workflow
This workflow takes raw un-skull-stripped volumes in 2 groups/populations and an ROI index to generate a color-coded shape object using the per-vertex local p-values of some local shape-measure (e.g., displacement, atrophy, Jacobian, curvature, etc.) a scene file containing the models of the ROIs where the 2 groups are different. It also outputs the p-values color-map superimposed on the mean shape.
Problem addressed by this workflow
Identifying local group differences between 2 populations in one specific ROI. This workflow also generates mean-shape DX model and a CCBBM *.m file with vertex-coloring using p-values, which can be viewed as a scene in ShapeViewer. The p-values color-map is:
- Red: p<=0.05
- Green: 0.05<p<=0.1
- Blue: p>0.1.
Detailed Workflow Usage & Specifications
- Input: Any volumetric file format can be used for the raw unskull-stripped imput images (e.g., DICOM, Directories, Analyze, MNC, Nifti, etc.)
- Pre-processing
- Image conversion to Analyze
- Converting to 8-bit
- Intensity Inhomogeneity correction
- Skull-stripping
- Affine Registration
- Auto Volume Parsing
- Shape Modeling and Refinement
- Computing the local Mean and Gaussian curvatures per vertex on the shape of choice for all subjects
- Shape registration - mapping, shape-correspondences
- Mean (atlas) shape generation
- (Non-parametric) permutation test for statistical differences between the 2 groups locally computed on each vertex of the corresponding shapes
- Color-coding (mapping) the p-values on the mean-shape (shape-atlas)
Pipeline Workflows
Footnotes
| META FILEATTACHMENT | Automated_LSA.pipe | attr="h" comment="" date="1257291910" path="Automated_LSA.pipe" size="2116285" user="dinov" version="1.1" |
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| META FILEATTACHMENT | Automated_LSA_Fig1.png | attr="h" comment="" date="1257291956" path="Automated_LSA_Fig1.png" size="821138" user="dinov" version="1.1" |
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