{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:14:28Z","timestamp":1787015668882,"version":"build-2736575974"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T00:00:00Z","timestamp":1739059200000},"content-version":"vor","delay-in-days":39,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroimaging"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Purpose<\/jats:title>\n                    <jats:p>Accurate and consistent lesion segmentation from magnetic resonance imaging is required for longitudinal multiple sclerosis (MS) data analysis. In this work, we propose two new transfer learning\u2010based pipelines to improve segmentation performance for subjects in longitudinal MS datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>In general, transfer learning is used to improve deep learning model performance for the unseen dataset by fine\u2010tuning a pretrained model with a limited number of labeled scans from the unseen dataset. The proposed methodologies fine\u2010tune the deep learning model for each subject using the first scan and improve segmentation performance for later scans for the same subject. We also investigated the statistical benefits of the proposed methodology by modeling lesion volume over time between progressors according to confirmed disability progression and nonprogressors for a large in\u2010house dataset (937 MS patients, 3210 scans) using a linear mixed effect (LME) model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results show statistically significant improvement for the proposed methodology compared with the traditional transfer learning method using Dice (improvement: 2%), sensitivity (6%), and average volumetric difference (16%), as well as visual analysis for public and in\u2010house datasets. The LME result showed that the proposed subject\u2010wise transfer learning method had increased statistical power for the measurement of longitudinal lesion volume.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed method improved lesion segmentation performance and can reduce manual effort to correct the automatic segmentations for final data analysis in longitudinal studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jon.70024","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T01:42:23Z","timestamp":1739238143000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Subject\u2010Based Transfer Learning in Longitudinal Multiple Sclerosis Lesion Segmentation"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6997-5717","authenticated-orcid":false,"given":"Sibaji","family":"Gaj","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Lerner Research Institute Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhaskar","family":"Thoomukuntla","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Lerner Research Institute Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Ontaneda","sequence":"additional","affiliation":[{"name":"Mellen Center for Multiple Sclerosis and Research, Neurological Institute Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7833-8138","authenticated-orcid":false,"given":"Kunio","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Lerner Research Institute Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,2,9]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000000768"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000007035"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(15)00393-2"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.10.011"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528821"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.06.006"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.034"},{"key":"e_1_2_8_9_1","unstructured":"S.Roy J. A.Butman D. S.Reich et\u00a0al. \u201cMultiple Sclerosis Lesion Segmentation From Brain MRI via Fully convolutional Neural Networks \u201d preprint arXiv:180309172 2018https:\/\/arxiv.org\/abs\/1803.09172."},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2021.102854"},{"key":"e_1_2_8_11_1","doi-asserted-by":"crossref","unstructured":"H.Zhang A. M.Valcarcel R.Bakshi et\u00a0al. \u201cMultiple Sclerosis Lesion Segmentation With Tiramisu and 2.5 D Stacked Slices \u201d inMedical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference(Springer 2019) 338\u2013346.","DOI":"10.1007\/978-3-030-32248-9_38"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"J.Zhang L.Zuo B. E.Dewey et\u00a0al. \u201cTowards an Accurate and Generalizable Multiple Sclerosis Lesion Segmentation Model Using Self\u2010Ensembled Lesion Fusion \u201d in2024 IEEE International Symposium on Biomedical Imaging (ISBI)(ISBI 2024) 1\u20135.","DOI":"10.1109\/ISBI56570.2024.10635877"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.12.064"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.101638"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"M.Ghafoorian A.Mehrtash T.Kapur et\u00a0al. \u201cTransfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation \u201d inMedical Image Computing and Computer Assisted Intervention\u2212MICCAI 2017: 20th International Conference (Springer 2017) 516\u2013524.https:\/\/arxiv.org\/abs\/1702.07841.","DOI":"10.1007\/978-3-319-66179-7_59"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/1352458518801479"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.668698"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1097\/00004728-199403000-00005"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.07.033"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.012"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_2_8_22_1","first-page":"234","volume-title":"International Conference on Medical Image Computing and Computer\u2010Assisted Intervention","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172322"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28111"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1177\/1352458520921364"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255939"},{"key":"e_1_2_8_28_1","unstructured":"F.Yu A.Seff Y.Zhang et\u00a0al. \u201cLSUN: Construction of a Large\u2010Scale Image Dataset Using Deep Learning With Humans in the Loop \u201d preprint arXiv:150603365 2015 https:\/\/arxiv.org\/abs\/1506.03365."},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1167\/9.8.1037"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2022.103644"}],"container-title":["Journal of Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jon.70024","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/jon.70024","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jon.70024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:22:25Z","timestamp":1773044545000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jon.70024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1111\/jon.70024"],"URL":"https:\/\/doi.org\/10.1111\/jon.70024","archive":["Portico"],"relation":{},"ISSN":["1051-2284","1552-6569"],"issn-type":[{"value":"1051-2284","type":"print"},{"value":"1552-6569","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-03-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70024"}}