{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T18:14:27Z","timestamp":1793211267034,"version":"4.5.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and efficient automated approach can rapidly provide information about the patient. Here, a convolutional neural network-based method is proposed to segment lesions from FLAIR images. The DenseLessystem includes two stages: pre-processing of image data (brain extraction, standardization), then segmentation of MS lesions using an end-to-end slice-wise dense network. We also identified the segmented lesions in specific locations [periventricular, (juxta)cortical, infratentorial, and spinal]. DenseLesis evaluated and compared to other methods on our assembled data and the public MSSEG 2016 MS challenge dataset. Our model demonstrates a significant improvement in segmentation quality over previous approaches, achieving an average Dice score of 0.80% on the Szeged MS dataset. On the MSSEG 2016 dataset, our method achieved Dice scores ranging from 0.32% to 0.73%, comparable to those of human raters.<\/jats:p>","DOI":"10.3389\/fneur.2026.1704317","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T05:29:34Z","timestamp":1772083774000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification"],"prefix":"10.3389","volume":"17","author":[{"given":"Melinda","family":"Katona","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Szeged","place":["Szeged, Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bence","family":"Bozsik","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Szeged","place":["Szeged, 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