Population average atlas for RecobundlesX (BundleSeg) - TractSeg Definitions

<p><strong>Multi-atlas bundle segmentation</strong></p> <p>This data is made to be used with the following script:<br><a href="https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py">https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py</a></p> <blockquote> <p><br>Etienne St-Onge, Kurt Schilling, Francois Rheault, "BundleSeg: A versatile, reliable and reproducible approach to whitte matter bundle segmentation.", arXiv, 2308.10958 (2023)<br>Rheault, Fran&ccedil;ois. "Analyse et reconstruction de faisceaux de la mati&egrave;re blanche." Computer Science (Universit&eacute; de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255</p> </blockquote> <p><strong>Usage</strong><br>Here is an example (for more details use `scil_tractogram_segment_with_bundleseg.py -h`) :</p> <p><code>antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4</code><br><code>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</code><br><br></p> <p>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 <a href="https://github.com/imeka/mi-brain">https://github.com/imeka/mi-brain</a></p> <p>(If you are processing multiple subjects, this pipeline could be useful for you <a href="https://github.com/scilus/rbx_flow">https://github.com/scilus/rbx_flow</a>)</p> <p><strong>Notes on bundles</strong><br>- 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.<br>- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track&nbsp;<br>- 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).<br>- 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.</p>