Population average atlas for RecobundlesX (BundleSeg) - TractSeg Definitions

Multi-atlas bundle segmentation

This data is made to be used with the following script:
https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py

Etienne St-Onge, Kurt Schilling, Francois Rheault, "BundleSeg: A versatile, reliable and reproducible approach to whitte matter bundle segmentation.", arXiv, 2308.10958 (2023)
Rheault, François. "Analyse et reconstruction de faisceaux de la matière blanche." Computer Science (Université de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255

Usage
Here is an example (for more details use `scil_tractogram_segment_with_bundleseg.py -h`) :

antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4
scil_tractogram_segment_with_bundleseg.py ${TRACTOGRAM} config_fss_1.json atlas/ output0GenericAffine.mat --out_dir ${OUTPUT_DIR}/ --log_level DEBUG --processes 8 --seeds 0 --inverse -f

To facilitate interpretation, all endpoints were uniformized head/tail. To see, which side of a bundle is head or tail, you can load the atlas bundle into the software MI-Brain https://github.com/imeka/mi-brain

(If you are processing multiple subjects, this pipeline could be useful for you https://github.com/scilus/rbx_flow)

Notes on bundles
- The bundles follow the overall anatomical definition of TractSeg (initially from TractQuerier) but are a heavily processed union to discard false positives, outliers, unrealistic paths, etc.
- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track 
- The cerebellum is often cut due to acquisition FOV. In such a case, all projection bundles will be more difficult to recognize and most cerebellum bundles will be missing (ICP, MCP, SCP).
- All the bundles starting with T_ (Thalamo) or ST_ (Striato) are based on region of interest, and are not usually part of classical major pathways.