{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T13:28:40Z","timestamp":1790947720615,"version":"4.1.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":120,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-10-COHO-002"],"award-info":[{"award-number":["ANR-10-COHO-002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Spain Ministry of Science and Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003741","name":"Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003741","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007379","name":"Fondation pour l'Aide \u00e0 la Recherche sur la Scl\u00e9rose en Plaques","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007379","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage: Clinical"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.nicl.2025.103795","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T05:15:43Z","timestamp":1747718143000},"page":"103795","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation"],"prefix":"10.1016","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1737-1345","authenticated-orcid":false,"given":"Luisana","family":"\u00c1lvarez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergi","family":"Valverde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c0lex","family":"Rovira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xavier","family":"Llad\u00f3","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.nicl.2025.103795_b1","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.1007453","article-title":"New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation","volume":"16","author":"Basaran","year":"2022","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.nicl.2025.103795_b2","series-title":"Towards continual learning in medical imaging","author":"Baweja","year":"2018"},{"key":"10.1016\/j.nicl.2025.103795_b3","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","first-page":"165","article-title":"Culprit-prune-net: Efficient continual sequential multi-domain learning with application to skin lesion classification","author":"Bayasi","year":"2021"},{"key":"10.1016\/j.nicl.2025.103795_b4","series-title":"CAIBDA 2022; 2nd International Conference on Artificial Intelligence, Big Data and Algorithms","first-page":"1","article-title":"Breast cancer detection model training strategy based on continual learning","author":"Chen","year":"2022"},{"key":"10.1016\/j.nicl.2025.103795_b5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ncl.2023.06.008","article-title":"Recent advances in diagnostic, prognostic, and disease-monitoring biomarkers in multiple sclerosis","volume":"42","author":"Chertcoff","year":"2024","journal-title":"Neurol. Clin."},{"key":"10.1016\/j.nicl.2025.103795_b6","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","first-page":"424","article-title":"3D u-net: Learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.nicl.2025.103795_b7","series-title":"MICCAI 2021-24th International Conference on Medical Image Computing and Computer Assisted Intervention","article-title":"Msseg-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure","author":"Commowick","year":"2021"},{"key":"10.1016\/j.nicl.2025.103795_b8","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1016\/S0140-6736(08)61620-7","article-title":"Multiple sclerosis","volume":"372","author":"Compston","year":"2008","journal-title":"Lancet"},{"key":"10.1016\/j.nicl.2025.103795_b9","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TMI.2007.906087","article-title":"An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images","volume":"27","author":"Coupe","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.nicl.2025.103795_b10","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.8.1.014504","article-title":"Investigating efficient CNN architecture for multiple sclerosis lesion segmentation","volume":"8","author":"Fenneteau","year":"2021","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.nicl.2025.103795_b11","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000013016","article-title":"Performance of the 2017 and 2010 revised mcdonald criteria in predicting ms diagnosis after a clinically isolated syndrome: A magnims study","volume":"98","author":"Filippi","year":"2022","journal-title":"Neurology"},{"key":"10.1016\/j.nicl.2025.103795_b12","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","article-title":"Unbiased average age-appropriate atlases for pediatric studies","volume":"54","author":"Fonov","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.nicl.2025.103795_b13","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/S1053-8119(09)70884-5","article-title":"Unbiased nonlinear average age-appropriate brain templates from birth to adulthood","volume":"47","author":"Fonov","year":"2009","journal-title":"NeuroImage"},{"key":"10.1016\/j.nicl.2025.103795_b14","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","article-title":"Harmonization of cortical thickness measurements across scanners and sites","volume":"167","author":"Fortin","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.nicl.2025.103795_b15","series-title":"Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation","first-page":"516","author":"Ghafoorian","year":"2017"},{"key":"10.1016\/j.nicl.2025.103795_b16","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering11080858","article-title":"Contrast-enhancing lesion segmentation in multiple sclerosis: A deep learning approach validated in a multicentric cohort","volume":"11","author":"Greselin","year":"2024","journal-title":"Bioengineering"},{"key":"10.1016\/j.nicl.2025.103795_b17","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain adaptation for medical image analysis: A survey","volume":"69","author":"Guan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.nicl.2025.103795_b18","doi-asserted-by":"crossref","first-page":"4952","DOI":"10.1002\/hbm.24750","article-title":"Automated brain extraction of multisequence mri using artificial neural networks","volume":"40","author":"Isensee","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.nicl.2025.103795_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102078","article-title":"Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations","volume":"116","author":"Karimi","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.nicl.2025.103795_b20","series-title":"Segmentation of multiple sclerosis lesions across hospitals: Learn continually or train from scratch?","author":"Karthik","year":"2022"},{"key":"10.1016\/j.nicl.2025.103795_b21","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.nicl.2025.103795_b22","doi-asserted-by":"crossref","first-page":"4102","DOI":"10.1038\/s41598-023-31207-5","article-title":"Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis","volume":"13","author":"Krishnan","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.nicl.2025.103795_b23","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1109\/TMI.2019.2905770","article-title":"Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge","volume":"38","author":"Kuijf","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.nicl.2025.103795_b24","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.nicl.2025.103795_b25","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TMI.2022.3211195","article-title":"Domain-incremental cardiac image segmentation with style-oriented replay and domain-sensitive feature whitening","volume":"42","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.nicl.2025.103795_b26","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.ins.2011.10.011","article-title":"Segmentation of multiple sclerosis lesions in brain mri: A review of automated approaches","volume":"186","author":"Llad\u00f3","year":"2012","journal-title":"Inform. Sci."},{"key":"10.1016\/j.nicl.2025.103795_b27","series-title":"Shifts 2.0: Extending the dataset of real distributional shifts","author":"Malinin","year":"2022"},{"key":"10.1016\/j.nicl.2025.103795_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.msard.2022.104187","article-title":"Assessment of T2 lesion-based disease activity volume outcomes in predicting disease progression in multiple sclerosis over 10 years","volume":"67","author":"Oship","year":"2022","journal-title":"Mult. Scler. Relat. Disord."},{"key":"10.1016\/j.nicl.2025.103795_b29","doi-asserted-by":"crossref","first-page":"5678","DOI":"10.1038\/s41467-021-25858-z","article-title":"Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging","volume":"12","author":"Perkonigg","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.nicl.2025.103795_b30","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1148\/radiol.2020200038","article-title":"Continuous learning ai in radiology: Implementation principles and early applications","volume":"297","author":"Pianykh","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.nicl.2025.103795_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116450","article-title":"Harmonization of large mri datasets for the analysis of brain imaging patterns throughout the lifespan","volume":"208","author":"Pomponio","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.nicl.2025.103795_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107021","article-title":"Boosting multiple sclerosis lesion segmentation through attention mechanism","volume":"161","author":"Rondinella","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.nicl.2025.103795_b33","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s00234-015-1552-2","article-title":"A toolbox for multiple sclerosis lesion segmentation","volume":"57","author":"Roura","year":"2015","journal-title":"Neuroradiology"},{"key":"10.1016\/j.nicl.2025.103795_b34","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.nicl.2017.11.015","article-title":"A supervised framework with intensity subtraction and deformation field features for the detection of new t2-w lesions in multiple sclerosis","volume":"17","author":"Salem","year":"2018","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.nicl.2025.103795_b35","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.1007619","article-title":"Improving the detection of new lesions in multiple sclerosis with a cascaded 3d fully convolutional neural network approach","volume":"16","author":"Salem","year":"2022","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.nicl.2025.103795_b36","article-title":"A fully convolutional neural network for new t2-w lesion detection in multiple sclerosis","volume":"25","author":"Salem","year":"2020","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.nicl.2025.103795_b37","series-title":"Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health","first-page":"226","article-title":"Continual domain incremental learning for chest x-ray classification in low-resource clinical settings","author":"Srivastava","year":"2021"},{"key":"10.1016\/j.nicl.2025.103795_b38","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4itk: Improved n3 bias correction","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.nicl.2025.103795_b39","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neuroimage.2017.04.034","article-title":"Improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach","volume":"155","author":"Valverde","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.nicl.2025.103795_b40","article-title":"One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks","volume":"21","author":"Valverde","year":"2019","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.nicl.2025.103795_b41","series-title":"Towards continuous learning for glioma segmentation with elastic weight consolidation","author":"van Garderen","year":"2019"},{"key":"10.1016\/j.nicl.2025.103795_b42","article-title":"Scanner-specific optimisation of automated lesion segmentation in ms","volume":"44","author":"van Nederpelt","year":"2024","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.nicl.2025.103795_b43","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1188336","article-title":"3D u-net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies","volume":"17","author":"Wahlig","year":"2023","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.nicl.2025.103795_b44","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/S1474-4422(13)70124-8","article-title":"Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration","volume":"12","author":"Wardlaw","year":"2013","journal-title":"Lancet Neurol."},{"key":"10.1016\/j.nicl.2025.103795_b45","article-title":"Lst-ai: A deep learning ensemble for accurate ms lesion segmentation","volume":"42","author":"Wiltgen","year":"2024","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.nicl.2025.103795_b46","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3013","article-title":"Der: Dynamically expandable representation for class incremental learning","author":"Yan","year":"2021"}],"container-title":["NeuroImage: Clinical"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2213158225000658?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2213158225000658?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:14:37Z","timestamp":1762607677000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2213158225000658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":46,"alternative-id":["S2213158225000658"],"URL":"https:\/\/doi.org\/10.1016\/j.nicl.2025.103795","relation":{},"ISSN":["2213-1582"],"issn-type":[{"value":"2213-1582","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation","name":"articletitle","label":"Article Title"},{"value":"NeuroImage: Clinical","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.nicl.2025.103795","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"103795"}}