{"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/comparison-of-distances-for-supervised","title":"Comparison of Distances for Supervised Segmentation of White Matter Tractography","arxiv_id":"1708.01440","date":"2017-08-04","proceeding":null,"authors":["Emanuele Olivetti","Giulia Bertò","Pietro Gori","Nusrat Sharmin","Paolo Avesani"],"abstract":"Tractograms are mathematical representations of the main paths of axons\nwithin the white matter of the brain, from diffusion MRI data. Such\nrepresentations are in the form of polylines, called streamlines, and one\nstreamline approximates the common path of tens of thousands of axons. The\nanalysis of tractograms is a task of interest in multiple fields, like\nneurosurgery and neurology. A basic building block of many pipelines of\nanalysis is the definition of a distance function between streamlines. Multiple\ndistance functions have been proposed in the literature, and different authors\nuse different distances, usually without a specific reason other than invoking\nthe \"common practice\". To this end, in this work we want to test such common\npractices, in order to obtain factual reasons for choosing one distance over\nanother. For these reasons, in this work we compare many streamline distance\nfunctions available in the literature. We focus on the common task of automatic\nbundle segmentation and we adopt the recent approach of supervised segmentation\nfrom expert-based examples. Using the HCP dataset, we compare several distances\nobtaining guidelines on the choice of which distance function one should use\nfor supervised bundle segmentation.","url_abs":"http://arxiv.org/abs/1708.01440v1","url_pdf":"http://arxiv.org/pdf/1708.01440v1.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":"comparison-of-distances-for-supervised","repo_url":"https://github.com/emanuele/prni2017_comparison_of_distances","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}