{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:49:10Z","timestamp":1775890150942,"version":"3.50.1"},"posted":{"date-parts":[[2025,1,10]]},"group-title":"Neuroscience","reference-count":62,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/openneuro.org\/datasets\/ds005143\/versions\/1.3.0\">https:\/\/openneuro.org\/datasets\/ds005143\/versions\/1.3.0<\/jats:ext-link>\n                  , and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/sct-pipeline\/fmri-segmentation\/\">https:\/\/github.com\/sct-pipeline\/fmri-segmentation\/<\/jats:ext-link>\n                  , and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.\n                <\/jats:p>","DOI":"10.1101\/2025.01.07.631402","type":"posted-content","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T17:00:17Z","timestamp":1736528417000},"source":"Crossref","is-referenced-by-count":1,"title":["EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8492-6959","authenticated-orcid":false,"given":"Rohan","family":"Banerjee","sequence":"first","affiliation":[{"name":"Department of Computer Science, Polytechnique Montreal, Montreal, Quebec, Canada"},{"name":"Mila - Quebec AI Institute, Montreal, Quebec, Canada"},{"name":"NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8187-4911","authenticated-orcid":false,"given":"Merve","family":"Kaptan","sequence":"additional","affiliation":[{"name":"Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9646-0201","authenticated-orcid":false,"given":"Alexandra","family":"Tinnermann","sequence":"additional","affiliation":[{"name":"Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-0499","authenticated-orcid":false,"given":"Ali","family":"Khatibi","sequence":"additional","affiliation":[{"name":"McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada"},{"name":"Centre de recherche de l\u2019Institut Universitaire de G\u00e9riatrie de Montr\u00e9al, Montr\u00e9al, Quebec, Canada"},{"name":"Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), University of Birmingham, UK"},{"name":"Centre for Human Brain Health, University of Birmingham, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9515-6810","authenticated-orcid":false,"given":"Alice","family":"Dabbagh","sequence":"additional","affiliation":[{"name":"Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 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