{"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/a-longitudinal-method-for-simultaneous-whole","title":"A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis","arxiv_id":"2008.05117","date":"2020-08-12","proceeding":null,"authors":["Stefano Cerri","Andrew Hoopes","Douglas N. Greve","Mark Mühlau","Koen van Leemput"],"abstract":"In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon.","url_abs":"https://arxiv.org/abs/2008.05117v2","url_pdf":"https://arxiv.org/pdf/2008.05117v2.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":"a-longitudinal-method-for-simultaneous-whole","repo_url":"https://github.com/freesurfer/freesurfer/tree/dev/samseg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-medical-imaging-segmentation","task_name":"3D Medical Imaging Segmentation"},{"task_slug":"brain-image-segmentation","task_name":"Brain Image Segmentation"},{"task_slug":"brain-lesion-segmentation-from-mri","task_name":"Brain Lesion Segmentation From Mri"},{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"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}