Difference between revisions of "2010 Winter Project Week ProstateSeg"
From NAMIC Wiki
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*****input: two images from ShapeBased\_reg\trainingShapes | *****input: two images from ShapeBased\_reg\trainingShapes | ||
*****output: transformed moving image in uchar nrrd image format | *****output: transformed moving image in uchar nrrd image format | ||
− | ***make anisotropic | + | ***make anisotropic (maybe this step could be skipped, to have an atlas with isotropic images) |
− | |||
***results are copied to ShapeBased\_reg\alignTrainingShapes | ***results are copied to ShapeBased\_reg\alignTrainingShapes | ||
**alignTrainingShapesNonIso.bash: faster but not that accurate | **alignTrainingShapesNonIso.bash: faster but not that accurate |
Revision as of 17:00, 6 January 2010
Home < 2010 Winter Project Week ProstateSeg
Key Investigators
- Andras Lasso, Gabor Fichtinger (Queen's University)
- Yi Gao, Allen Tannenbaum (Georgia Tech)
- Andriy Fedorov (BWH)
Objective
Implement a Slicer module from the shape-based prostate segmentation algorithm developed by Yi Gao et al.
Approach, Plan
Implement as a command-line module that can be downloaded and installed as a Slicer extension. Add automatic testing.
Progress
The algorithm can be compiled using CMake on both linux and windows, test data are available.
References
- Yi Gao, Romeil Sandhu, Gabor Fichtinger, Allen Tannenbaum, A Coupled Global Registration and Segmentation Framework with Application to the Magnetic Resonance Prostate Imagery, IEEE Trans Med Imaging (in review)
Notes
Training
- Registration
- alignTrainingShapes.bash (execution time is about 30min)
- make isotropic (z direction)
- register
- ShapeBased\_reg\src\imageRegByPointSet\c\affine\ => 1 executable
- pairwise image registration (there are many supporting files in ShapeBased\_reg\src; the result is one executable)
- input: two images from ShapeBased\_reg\trainingShapes
- output: transformed moving image in uchar nrrd image format
- ShapeBased\_reg\src\imageRegByPointSet\c\affine\ => 1 executable
- make anisotropic (maybe this step could be skipped, to have an atlas with isotropic images)
- results are copied to ShapeBased\_reg\alignTrainingShapes
- alignTrainingShapesNonIso.bash: faster but not that accurate
- alignTrainingShapes.bash (execution time is about 30min)
- Convert from binary to level set
- ShapeBased\_reg\alignTrainingShapes\toSFLS\ => 1 executable
- Input: nrrd binary image
- Output: level set description
- For each binary image a level set image is generated and saved to ShapeBased\_reg\alignTrainingShapes\toSFLS
- ShapeBased\_reg\alignTrainingShapes\toSFLS\ => 1 executable
- Learning using PCA
- ProstateSeg\ShapeBased\_reg\alignTrainingShapes\toSFLS\learn => 1 executable
- Input: shapeList.txt list of all level set files
- Output: mean shape and i-th eigen shape (multiplied by the eigen value),
- Execution time is about 1 minute, repeated for each eigen shape
- Images are flipped, but the images to be segmented (or the training shapes) could be flipped instead.
Segmentation
- ProstateSeg\ShapeBased\version20091203 => 1 executable (wholeseg)
- Input: image to be segmented, and two points (at the left and right side of the prostate, on a center axial slice in IJK space)
- Output: ?