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Manual segmentation is time\u2010consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2\u2010weighted fluid\u2010attenuated inversion recovery (FLAIR) MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning\u2010based MS lesion segmentation algorithm based on the nnU\u2010Net 3D full\u2010resolution U\u2010Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG\u20102 (\n                      <jats:italic>n<\/jats:italic>\n                      = 14), MSLesSeg (\n                      <jats:italic>n<\/jats:italic>\n                      = 51), and a clinical cohort (\n                      <jats:italic>n<\/jats:italic>\n                      = 10), and compared to SAMSEG, LST\u2010LPA, and LST\u2010AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly to or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm\n                      <jats:sup>3<\/jats:sup>\n                      , whereas the benchmark methods missed larger lesions in addition to smaller ones.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>FLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jon.70085","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T10:24:52Z","timestamp":1757672692000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1177-2886","authenticated-orcid":false,"given":"Emma","family":"Dereskewicz","sequence":"first","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9224-4664","authenticated-orcid":false,"given":"Francesco","family":"La Rosa","sequence":"additional","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York USA"},{"name":"Windreich Department of Artificial Intelligence &amp; Human Health Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-333X","authenticated-orcid":false,"given":"Jonadab","family":"Dos Santos Silva","sequence":"additional","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Sizer","sequence":"additional","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Kohli","sequence":"additional","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxence","family":"Wynen","sequence":"additional","affiliation":[{"name":"ICTeam, Universit\u00e9 Catholique De Louvain  Louvain\u2010la\u2010Neuve Belgium"},{"name":"Neuroinflammation Imaging Lab, Universit\u00e9 Catholique de Louvain  Brussels Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William A.","family":"Mullins","sequence":"additional","affiliation":[{"name":"Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Maggi","sequence":"additional","affiliation":[{"name":"Neuroinflammation Imaging Lab, Universit\u00e9 Catholique de Louvain  Brussels Belgium"},{"name":"Cliniques Universitaires Saint\u2010Luc, Universit\u00e9 Catholique De Louvain  Brussels Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Levy","sequence":"additional","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamso","family":"Onyemeh","sequence":"additional","affiliation":[{"name":"Department of Neurology Icahn School of Medicine at Mount Sinai  New York New York 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