{"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/tractseg-fast-and-accurate-white-matter-tract","title":"TractSeg - Fast and accurate white matter tract segmentation","arxiv_id":"1805.07103","date":"2018-05-18","proceeding":null,"authors":["Jakob Wasserthal","Peter Neher","Klaus H. Maier-Hein"],"abstract":"The individual course of white matter fiber tracts is an important key for\nanalysis of white matter characteristics in healthy and diseased brains.\nUniquely, diffusion-weighted MRI tractography in combination with region-based\nor clustering-based selection of streamlines allows for the in-vivo delineation\nand analysis of anatomically well known tracts. This, however, currently\nrequires complex, computationally intensive and tedious-to-set-up processing\npipelines. TractSeg is a novel convolutional neural network-based approach that\ndirectly segments tracts in the field of fiber orientation distribution\nfunction (fODF) peaks without requiring tractography, image registration or\nparcellation. We demonstrate in 105 subjects from the Human Connectome Project\nthat the proposed approach is much faster than existing methods while providing\nunprecedented accuracy. The code and data are openly available at\nhttps://github.com/MIC-DKFZ/TractSeg/ and\nhttps://doi.org/10.5281/zenodo.1088277, respectively.","url_abs":"http://arxiv.org/abs/1805.07103v2","url_pdf":"http://arxiv.org/pdf/1805.07103v2.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":"tractseg-fast-and-accurate-white-matter-tract","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":"tractseg-fast-and-accurate-white-matter-tract","repo_url":"https://github.com/giulia-berto/app-extract-peaks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"tractseg-fast-and-accurate-white-matter-tract","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":"image-registration","task_name":"Image Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}