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 UsageHere 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 4scil_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.多图谱神经束分割（Multi-atlas bundle segmentation） 本数据集配套使用以下脚本：https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py ## 参考文献 1. Etienne St-Onge、Kurt Schilling、Francois Rheault，《BundleSeg：一种通用、可靠且可复现的白质神经束分割方法》，arXiv，2308.10958（2023年） 2. Francois Rheault，《白质神经束的分析与重建》，计算机科学（谢布鲁克大学）（2020年），https://savoirs.usherbrooke.ca/handle/11143/17255 ## 使用方法 下文为使用示例（如需获取详细参数说明，请运行`scil_tractogram_segment_with_bundleseg.py -h`）： bash 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 为便于解读，所有神经束的端点均已统一标记为头端/尾端。若需查看某神经束的头端与尾端位置，可将图谱神经束导入MI-Brain软件（https://github.com/imeka/mi-brain）。 若需处理多被试数据，可使用以下流程工具：https://github.com/scilus/rbx_flow ## 神经束相关说明 - 本数据集的神经束遵循TractSeg（最初源自TractQuerier）的整体解剖学定义，但经过了深度联合筛选，以剔除假阳性结果、异常值及不合理路径等。 - CG（皮质脊髓束）存在3种可能的端点位置，但其尾端的完整范围难以追踪。 - 由于采集视场（Field of View, FOV）的限制，小脑常被截断。在此情况下，所有投射神经束的识别难度将增加，且多数小脑相关神经束会缺失（包括ICP、MCP、SCP）。 - 所有以T_（丘脑，Thalamo）或ST_（纹状体，Striato）开头的神经束均基于感兴趣区（Region of Interest, ROI）构建，通常不属于经典主要神经通路。