This is similar to scil_bundle_pairwise_comparison.py, but for mask and atlas (as nifiti). This is an easy way to quantify the similarity between wmparc of bundle label maps or BET mask in scan-rescan or across subjects for example.
This is generating a JSON file that contains four measures: dice, adjacency_voxels, overlap and overreach.
Everything has to be in uint8 or uint16, coregistered nifti.
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Type of change
Check the relevant options.
[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
Provide data, screenshots, command line to test (if relevant)
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Checklist
[x] My code follows the style guidelines of this project (run autopep8)
[x] I added relevant citations to scripts, modules and functions docstrings and descriptions
[x] I have performed a self-review of my code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[x] My changes generate no new warnings
[ ] I moved all functions from the script file (except the argparser and main) to scilpy modules
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
Quick description
This is similar to scil_bundle_pairwise_comparison.py, but for mask and atlas (as nifiti). This is an easy way to quantify the similarity between wmparc of bundle label maps or BET mask in scan-rescan or across subjects for example.
This is generating a JSON file that contains four measures: dice, adjacency_voxels, overlap and overreach.
Everything has to be in uint8 or uint16, coregistered nifti.
...
Type of change
Check the relevant options.
Provide data, screenshots, command line to test (if relevant)
...
Checklist