{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tract-orientation-mapping-for-bundle-specific","title":"Tract orientation mapping for bundle-specific tractography","arxiv_id":"1806.05580","date":"2018-06-14","proceeding":null,"authors":["Jakob Wasserthal","Peter F. Neher","Klaus H. Maier-Hein"],"abstract":"While the major white matter tracts are of great interest to numerous studies\nin neuroscience and medicine, their manual dissection in larger cohorts from\ndiffusion MRI tractograms is time-consuming, requires expert knowledge and is\nhard to reproduce. Tract orientation mapping (TOM) is a novel concept that\nfacilitates bundle-specific tractography based on a learned mapping from the\noriginal fiber orientation distribution function (fODF) peaks to a list of\ntract orientation maps (also abbr. TOM). Each TOM represents one of the known\ntracts with each voxel containing no more than one orientation vector. TOMs can\nact as a prior or even as direct input for tractography. We use an\nencoder-decoder fully-convolutional neural network architecture to learn the\nrequired mapping. In comparison to previous concepts for the reconstruction of\nspecific bundles, the presented one avoids various cumbersome processing steps\nlike whole brain tractography, atlas registration or clustering. We compare it\nto four state of the art bundle recognition methods on 20 different bundles in\na total of 105 subjects from the Human Connectome Project. Results are\nanatomically convincing even for difficult tracts, while reaching low angular\nerrors, unprecedented runtimes and top accuracy values (Dice). Our code and our\ndata are openly available.","url_abs":"http://arxiv.org/abs/1806.05580v1","url_pdf":"http://arxiv.org/pdf/1806.05580v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tract-orientation-mapping-for-bundle-specific","repo_url":"https://github.com/MIC-DKFZ/TractSeg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"tract-orientation-mapping-for-bundle-specific","repo_url":"https://github.com/jelleman8/TractSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}