{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T01:41:07Z","timestamp":1789522867142,"version":"build-2803163510"},"reference-count":82,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007393","name":"Association Fran\u00e7aise contre les Myopathies","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007393","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010275","name":"Deutsche Gesellschaft f\u00fcr Muskelkranke","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010275","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011640","name":"European Neuromuscular Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011640","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002426","name":"Fondazione Telethon","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002426","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010767","name":"Stiftung f\u00fcr die Erforschung der Muskelkrankheiten","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004243","name":"Prinses Beatrix Spierfonds","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004243","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011641","name":"Muskelsvindfonden","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011641","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020796","name":"SMA Europe","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020796","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015362","name":"Amicus Therapeutics","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015362","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","nmd-journal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neuromuscular Disorders"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.nmd.2025.106304","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T23:56:22Z","timestamp":1765238182000},"page":"106304","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["286th ENMC international workshop: Muscle imaging: artificial intelligence, automatic segmentation and imaging data sharing in neuromuscular disease. Hoofddorp, The Netherlands, 7-9 March 2025"],"prefix":"10.1016","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0187-2199","authenticated-orcid":false,"given":"Jodi","family":"Warman-Chardon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volker","family":"Straub","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Vissing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Schlaeger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hermien E.","family":"Kan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre G.","family":"Carlier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristl","family":"Claeys","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martijn","family":"Froeling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hermien E","family":"Kan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Monforte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasper","family":"Morrow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Pichiecchio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanne S.","family":"Rauh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harmen","family":"Reyngoudt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Santini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Schlaeger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lara","family":"Schlaffke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volker","family":"Straub","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Tasca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Thornton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Vissing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glenn","family":"Walter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jodi","family":"Warman-Chardon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.nmd.2025.106304_bib0001","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2021.625308","article-title":"Overview of MR image segmentation strategies in neuromuscular disorders","volume":"12","author":"Ogier","year":"2021","journal-title":"Front Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0002","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/j.nmd.2020.08.360","article-title":"247th ENMC International Workshop: muscle magnetic resonance imaging - implementing muscle MRI as a diagnostic tool for rare genetic myopathy cohorts. Hoofddorp, The Netherlands","volume":"30","author":"Warman-Chardon","year":"2020","journal-title":"Neuromuscul Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0003","doi-asserted-by":"crossref","DOI":"10.1007\/s00415-021-10957-0","article-title":"Muscle MRI characteristic pattern for late-onset TK2 deficiency diagnosis","author":"Dom\u00ednguez-Gonz\u00e1lez","year":"2022","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0004","doi-asserted-by":"crossref","DOI":"10.1136\/jnnp-2022-328921","article-title":"Genotype-phenotype correlations in valosin-containing protein disease: a retrospective muticentre study","author":"Schiava","year":"2022","journal-title":"J Neurol Neurosurg Psychiatr"},{"key":"10.1016\/j.nmd.2025.106304_bib0005","series-title":"Quantitative vs qualitative muscle MRI: imaging biomarker in patients with oculopharyngeal muscular dystrophy (OPMD)","author":"Melkus","year":"2022"},{"key":"10.1016\/j.nmd.2025.106304_bib0006","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1007\/s00415-017-8547-3","article-title":"Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy","volume":"264","author":"Burakiewicz","year":"2017","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0007","first-page":"1","article-title":"Exploration of new contrasts, targets, and MR imaging and spectroscopy techniques for neuromuscular disease - A workshop report of Working Group 3 of the Biomedicine and Molecular Biosciences COST action BM1304 MYO-MRI","volume":"6","author":"Strijkers","year":"2019","journal-title":"J Neuromuscul Dis"},{"key":"10.1016\/j.nmd.2025.106304_bib0008","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.nmd.2019.08.011","article-title":"MYO-MRI diagnostic protocols in genetic myopathies","volume":"29","author":"Warman Chardon","year":"2019","journal-title":"Neuromuscul Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0009","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s00415-019-09539-y","article-title":"European muscle MRI study in limb girdle muscular dystrophy type R1\/2A (LGMDR1\/LGMD2A)","volume":"267","author":"Barp","year":"2020","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0010","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1136\/jnnp-2017-316736","article-title":"MRI in sarcoglycanopathies: a large international cohort study","volume":"89","author":"Tasca","year":"2018","journal-title":"J Neurol Neurosurg Psychiatr"},{"key":"10.1016\/j.nmd.2025.106304_bib0011","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1007\/s00415-017-8574-0","article-title":"Magnetic resonance imaging of leg muscles in patients with myotonic dystrophies","volume":"264","author":"Peric","year":"2017","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0012","doi-asserted-by":"crossref","DOI":"10.1177\/1756286420985256","article-title":"The value of qualitative muscle MRI in the diagnostic procedures of myopathies: a biopsy-controlled study in 191 patients","volume":"14","author":"Lehmann Urban","year":"2021","journal-title":"Ther Adv Neurol Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0013","doi-asserted-by":"crossref","DOI":"10.1001\/jamaneurol.2025.0992","article-title":"Quantitative muscle Magnetic resonance outcomes in patients with Duchenne muscular dystrophy: an exploratory analysis from the EMBARK randomized clinical trial","author":"Vandenborne","year":"2025","journal-title":"JAMA Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0014","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1002\/mus.27491","article-title":"Axial muscle involvement in patients with limb girdle muscular dystrophy type R9","volume":"65","author":"Revsbech","year":"2022","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.nmd.2025.106304_bib0015","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1007\/s00415-025-13062-8","article-title":"Natural history of facioscapulohumeral muscular dystrophy evaluated by multiparametric quantitative MRI: a prospective cohort study","volume":"272","author":"Paoletti","year":"2025","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0016","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000207833","article-title":"Muscle MRI in patients with oculopharyngeal muscular dystrophy","volume":"102","author":"Kroon","year":"2024","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0017","doi-asserted-by":"crossref","first-page":"997","DOI":"10.3233\/JND-240053","article-title":"Quantitative muscle MRI in sporadic inclusion body myositis (sIBM): a prospective cohort study","volume":"11","author":"Schlaffke","year":"2024","journal-title":"J Neuromuscul Dis"},{"key":"10.1016\/j.nmd.2025.106304_bib0018","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1186\/s41747-024-00475-9","article-title":"Development of an initial training and evaluation programme for manual lower limb muscle MRI segmentation","volume":"8","author":"Morrow","year":"2024","journal-title":"European Radiol Experiment"},{"key":"10.1016\/j.nmd.2025.106304_bib0019","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1136\/jnnp-2023-332454","article-title":"Quantitative MRI outcome measures in CMT1A using automated lower limb muscle segmentation","volume":"95","author":"O'Donnell","year":"2024","journal-title":"J Neurol Neurosurg Psychiatr"},{"key":"10.1016\/j.nmd.2025.106304_bib0020","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jot.2023.07.005","article-title":"Magnetic resonance imaging techniques for the quantitative analysis of skeletal muscle: state of the art","volume":"42","author":"Engelke","year":"2023","journal-title":"J Orthop Translat"},{"key":"10.1016\/j.nmd.2025.106304_bib0021","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.nmd.2022.09.010","article-title":"Quantitative vs qualitative muscle MRI: imaging biomarker in patients with oculopharyngeal muscular dystrophy (OPMD)","volume":"33","author":"Melkus","year":"2023","journal-title":"Neuromuscul Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0022","doi-asserted-by":"crossref","DOI":"10.1002\/acn3.51417","article-title":"Upper limb disease evolution in exon 53 skipping eligible patients with Duchenne muscular dystrophy","author":"Lilien","year":"2021","journal-title":"Ann Clin Transl Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0023","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1002\/ana.25804","article-title":"MRI in neuromuscular diseases: an emerging diagnostic tool and biomarker for prognosis and efficacy","volume":"88","author":"Dahlqvist","year":"2020","journal-title":"Ann Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0024","doi-asserted-by":"crossref","DOI":"10.1002\/jcsm.12987","article-title":"Three-year quantitative magnetic resonance imaging and phosphorus magnetic resonance spectroscopy study in lower limb muscle in dysferlinopathy","author":"Reyngoudt","year":"2022","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"10.1016\/j.nmd.2025.106304_bib0025","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2021.675781","article-title":"Different approaches to analyze muscle fat replacement with Dixon MRI in Pompe disease","volume":"12","author":"Alonso-Jim\u00e9nez","year":"2021","journal-title":"Front Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0026","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/s43856-022-00217-1","article-title":"Whole-muscle fat analysis identifies distal muscle end as disease initiation site in facioscapulohumeral muscular dystrophy","volume":"2","author":"Heskamp","year":"2022","journal-title":"Commun Med (Lond)"},{"key":"10.1016\/j.nmd.2025.106304_bib0027","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.nmd.2017.02.009","article-title":"Non-uniform muscle fat replacement along the proximodistal axis in Duchenne muscular dystrophy","volume":"27","author":"Hooijmans","year":"2017","journal-title":"Neuromuscul Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0028","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1002\/mus.26813","article-title":"Quantitative muscle MRI study of patients with sporadic inclusion body myositis","volume":"61","author":"Ansari","year":"2020","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.nmd.2025.106304_bib0029","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000009068","article-title":"Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies","author":"Verdu-Diaz","year":"2020","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0030","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1007\/s00415-021-10786-1","article-title":"Diagnostic magnetic resonance imaging biomarkers for facioscapulohumeral muscular dystrophy identified by machine learning","volume":"269","author":"Monforte","year":"2022","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0031","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1002\/jmri.28708","article-title":"The impact of fatty infiltration on MRI segmentation of lower limb muscles in neuromuscular diseases: a comparative study of deep learning approaches","volume":"58","author":"Hostin","year":"2023","journal-title":"J Magnet Resonan Imag"},{"key":"10.1016\/j.nmd.2025.106304_bib0032","first-page":"467","article-title":"Deep learning for automatic segmentation of thigh and leg muscles. Magnetic resonance materials in physics","volume":"35","author":"Agosti","year":"2022","journal-title":"Biol Med"},{"key":"10.1016\/j.nmd.2025.106304_bib0033","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1136\/jnnp-2018-319578","article-title":"Muscle MRI in a large cohort of patients with oculopharyngeal muscular dystrophy","volume":"90","author":"Alonso-Jimenez","year":"2019","journal-title":"J Neurol Neurosurg Psychiatr"},{"key":"10.1016\/j.nmd.2025.106304_bib0034","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0100292","article-title":"Upper girdle imaging in facioscapulohumeral muscular dystrophy","volume":"9","author":"Tasca","year":"2014","journal-title":"PloS One"},{"key":"10.1016\/j.nmd.2025.106304_bib0035","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1002\/mus.28235","article-title":"Skeletal muscle symptoms and quantitative MRI in females with dystrophinopathy","volume":"70","author":"Jenkins","year":"2024","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.nmd.2025.106304_bib0036","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1111\/ene.14446","article-title":"Deep phenotyping of facioscapulohumeral muscular dystrophy type 2 by magnetic resonance imaging","volume":"27","author":"Giacomucci","year":"2020","journal-title":"Eur J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0037","doi-asserted-by":"crossref","first-page":"e513","DOI":"10.1212\/WNL.0000000000012233","article-title":"Selection approach to identify the optimal biomarker using quantitative muscle MRI and functional assessments in Becker muscular dystrophy","volume":"97","author":"van de Velde","year":"2021","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0038","first-page":"151","article-title":"Multi-slice MRI reveals heterogeneity in disease distribution along the length of muscle in Duchenne muscular dystrophy","volume":"36","author":"Chrzanowski","year":"2017","journal-title":"Acta Myologica"},{"key":"10.1016\/j.nmd.2025.106304_bib0039","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1212\/WNL.0000000000004647","article-title":"Adding quantitative muscle MRI to the FSHD clinical trial toolbox","volume":"89","author":"Mul","year":"2017","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0040","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1002\/jcsm.13063","article-title":"Water T2 could predict functional decline in patients with dysferlinopathy","volume":"13","author":"Moore","year":"2022","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"10.1016\/j.nmd.2025.106304_bib0041","doi-asserted-by":"crossref","DOI":"10.1212\/NXG.0000000000200283","article-title":"Modeling of dysferlinopathy (LGMDR2) progression: a longitudinal fat fraction analysis","volume":"11","author":"Bolano-Diaz","year":"2025","journal-title":"Neurol Genet"},{"key":"10.1016\/j.nmd.2025.106304_bib0042","first-page":"S78","article-title":"P125 Quantitative MRI in upper limb muscles of patients with dysferlinopathy: 6-months and 12-months longitudinal data from the natural history Jain COS 2 project","volume":"33","author":"Wilson","year":"2023","journal-title":"Neuromuscul Disorder"},{"key":"10.1016\/j.nmd.2025.106304_bib0043","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.230337","article-title":"Sharing data is essential for the future of AI in medical imaging","volume":"6","author":"Bell","year":"2024","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.nmd.2025.106304_bib0044","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.3390\/tomography9050145","article-title":"Thirty years of the DICOM standard","volume":"9","author":"Larobina","year":"2023","journal-title":"Tomography"},{"key":"10.1016\/j.nmd.2025.106304_bib0045","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2016.44","article-title":"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments","volume":"3","author":"Gorgolewski","year":"2016","journal-title":"Sci Data"},{"key":"10.1016\/j.nmd.2025.106304_bib0046","first-page":"116","article-title":"Beyond the brain: MIDS extends BIDS to multiple modalities and anatomical regions","volume":"295","author":"Saborit-Torres","year":"2022","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.nmd.2025.106304_bib0047","article-title":"ORMIR-MIDS: an open standard for curating and sharing musculoskeletal imaging data; abstracts of the 24th quantitative musculoskeletal imaging workshop","volume":"9","author":"Bonaretti","year":"2025","journal-title":"J Bone Mineral Res"},{"key":"10.1016\/j.nmd.2025.106304_bib0048","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1007\/s00415-016-8123-2","article-title":"A novel clinical tool to classify facioscapulohumeral muscular dystrophy phenotypes","volume":"263","author":"Ricci","year":"2016","journal-title":"J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0049","doi-asserted-by":"crossref","DOI":"10.1038\/s41431-024-01770-0","article-title":"Double trouble: a comprehensive study into unrelated genetic comorbidities in adult patients with facioscapulohumeral muscular dystrophy type I","author":"Puma","year":"2025","journal-title":"Europe J Human Genet"},{"key":"10.1016\/j.nmd.2025.106304_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.jns.2025.123400","article-title":"High prevalence of facioscapulohumeral muscular dystrophy (FSHD) and inflammatory myopathies association: is there an interplay?","volume":"470","author":"Lauletta","year":"2025","journal-title":"J Neurol Sci"},{"key":"10.1016\/j.nmd.2025.106304_bib0051","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S1474-4422(15)00242-2","article-title":"MRI biomarker assessment of neuromuscular disease progression: a prospective observational cohort study","volume":"15","author":"Morrow","year":"2016","journal-title":"Lancet Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0052","doi-asserted-by":"crossref","first-page":"e1125","DOI":"10.1212\/WNL.0000000000006214","article-title":"Validation of MRC Centre MRI calf muscle fat fraction protocol as an outcome measure in CMT1A","volume":"91","author":"Morrow","year":"2018","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0053","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s12021-020-09485-5","article-title":"Musclesense: a trained, artificial neural network for the anatomical segmentation of lower limb magnetic resonance images in neuromuscular diseases","volume":"19","author":"Kanber","year":"2021","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.nmd.2025.106304_bib0054","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3233\/JND-140045","article-title":"The epidemiology of neuromuscular disorders: a comprehensive overview of the literature","volume":"2","author":"Deenen","year":"2015","journal-title":"J Neuromuscul Dis"},{"key":"10.1016\/j.nmd.2025.106304_bib0055","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1186\/1471-2458-14-1144","article-title":"A systematic review of barriers to data sharing in public health","volume":"14","author":"van Panhuis","year":"2014","journal-title":"BMC Public Health"},{"key":"10.1016\/j.nmd.2025.106304_bib0056","series-title":"Proceedings of the 20th international conference on artificial intelligence and statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.nmd.2025.106304_bib0057","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ejmp.2020.03.011","article-title":"Using federated data sources and Varian learning portal framework to train a neural network model for automatic organ segmentation","volume":"72","author":"Czeizler","year":"2020","journal-title":"Phys Med"},{"key":"10.1016\/j.nmd.2025.106304_bib0058","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.240097","article-title":"Deep anatomical federated network (Dafne): an open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation","author":"Santini","year":"2025","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.nmd.2025.106304_bib0059","doi-asserted-by":"crossref","first-page":"e4119","DOI":"10.1002\/nbm.4119","article-title":"Multi-center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles","volume":"32","author":"Schlaffke","year":"2019","journal-title":"NMR Biomed"},{"key":"10.1016\/j.nmd.2025.106304_bib0060","doi-asserted-by":"crossref","DOI":"10.21105\/joss.01204","article-title":"QMRTools: a Mathematica toolbox for quantitative MRI analysis","volume":"4","author":"Froeling","year":"2019","journal-title":"J Open Source Software"},{"key":"10.1016\/j.nmd.2025.106304_bib0061","doi-asserted-by":"crossref","unstructured":"Ronneberger O., Fischer P., Brox T., U-Net: convolutional networks for biomedical image segmentation. Vol. 9351. 2015. 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.nmd.2025.106304_bib0062","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.nmd.2025.106304_bib0063","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1111\/joa.13967","article-title":"Human lower leg muscles grow asynchronously","volume":"244","author":"Chow","year":"2024","journal-title":"J Anat"},{"key":"10.1016\/j.nmd.2025.106304_bib0064","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s00330-020-07075-2","article-title":"Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis","volume":"31","author":"Goodkin","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.nmd.2025.106304_bib0065","series-title":"Proceedings of the International Society for Magnetic Resonance in Medicine annual Meeting and Exhibition","article-title":"Clinical deployment of novel quantitative neuro-image analysis techniques using containerisation technology\u2019","author":"Moggridge","year":"2023"},{"key":"10.1016\/j.nmd.2025.106304_bib0066","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20190365","article-title":"The quantitative neuroradiology initiative framework: application to dementia","volume":"92","author":"Goodkin","year":"2019","journal-title":"Br J Radiol"},{"key":"10.1016\/j.nmd.2025.106304_bib0067","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2023.1200727","article-title":"Automated MRI quantification of volumetric per-muscle fat fractions in the proximal leg of patients with muscular dystrophies","volume":"14","author":"Huysmans","year":"2023","journal-title":"Front Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0068","doi-asserted-by":"crossref","first-page":"e638","DOI":"10.1212\/WNL.0000000000200708","article-title":"Prospective natural history study in 24 adult patients with LGMDR12 over 2 years of follow-up: quantitative MRI and clinical outcome measures","volume":"99","author":"De Wel","year":"2022","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0069","article-title":"Lessons for future clinical trials in adults with Becker muscular dystrophy: disease progression detected by muscle magnetic resonance imaging, clinical and patient-reported outcome measures","volume":"31","author":"De Wel","year":"2024","journal-title":"Eur J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0070","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2023.1105276","article-title":"Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease","volume":"14","author":"Colelli","year":"2023","journal-title":"Front Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0071","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1111\/j.1468-1331.2012.03753.x","article-title":"Muscle MRI in female carriers of dystrophinopathy","volume":"19","author":"Tasca","year":"2012","journal-title":"Eur J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0072","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.nmd.2017.12.002","article-title":"Hereditary myopathy with early respiratory failure (HMERF): still rare, but common enough","volume":"28","author":"Tasca","year":"2018","journal-title":"Neuromuscul Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0073","doi-asserted-by":"crossref","DOI":"10.1111\/ene.14446","article-title":"Deep phenotyping of facioscapulohumeral muscular dystrophy type 2 by magnetic resonance imaging","author":"Giacomucci","year":"2020","journal-title":"Eur J Neurol"},{"key":"10.1016\/j.nmd.2025.106304_bib0074","doi-asserted-by":"crossref","DOI":"10.1002\/jcsm.13815","article-title":"Myo-Guide: a Machine Learning-based web application for neuromuscular disease diagnosis with MRI","author":"Verdu-Diaz","year":"2025","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"10.1016\/j.nmd.2025.106304_bib0075","first-page":"2276","article-title":"Reliability and accuracy of skeletal muscle imaging in limb-girdle muscular dystrophies. Author response","volume":"80","author":"ten Dam","year":"2013","journal-title":"Neurology"},{"key":"10.1016\/j.nmd.2025.106304_bib0076","doi-asserted-by":"crossref","DOI":"10.1016\/j.nmd.2012.08.002","article-title":"TREAT-NMD workshop: pattern recognition in genetic muscle diseases using muscle MRI: 25-26 February 2011, Rome, Italy","author":"Straub","year":"2012","journal-title":"Neuromuscul Disord"},{"key":"10.1016\/j.nmd.2025.106304_bib0077","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s00117-024-01276-2","article-title":"[Expert recommendations for magnetic resonance imaging of muscle disorders]","volume":"64","author":"Zeng","year":"2024","journal-title":"Radiologie (Heidelb)"},{"key":"10.1016\/j.nmd.2025.106304_bib0078","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s00117-024-01276-2","article-title":"Expertenempfehlung zur magnetresonanztomographie bei Muskelerkrankungen","volume":"64","author":"Zeng","year":"2024","journal-title":"Die Radiologie"},{"key":"10.1016\/j.nmd.2025.106304_bib0079","doi-asserted-by":"crossref","first-page":"4264","DOI":"10.1007\/s00330-020-07487-0","article-title":"Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases","volume":"31","author":"Reyngoudt","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.nmd.2025.106304_bib0080","doi-asserted-by":"crossref","DOI":"10.1002\/nbm.70066","article-title":"A minimal annotation pipeline for deep learning segmentation of skeletal muscles","volume":"38","author":"Baudin","year":"2025","journal-title":"NMR Biomed"},{"key":"10.1016\/j.nmd.2025.106304_bib0081","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10334-024-01221-3","article-title":"Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases","volume":"38","author":"Martin","year":"2025","journal-title":"Magma"},{"key":"10.1016\/j.nmd.2025.106304_bib0082","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.nmd.2022.10.005","article-title":"265th ENMC International Workshop: muscle imaging in Facioscapulohumeral Muscular dystrophy (FSHD): relevance for clinical trials. 22\u201324 April 2022, Hoofddorp, The Netherlands","volume":"33","author":"Monforte","year":"2023","journal-title":"Neuromuscul Disorder"}],"container-title":["Neuromuscular Disorders"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0960896625010314?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0960896625010314?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:38:12Z","timestamp":1773704292000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0960896625010314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":82,"alternative-id":["S0960896625010314"],"URL":"https:\/\/doi.org\/10.1016\/j.nmd.2025.106304","relation":{},"ISSN":["0960-8966"],"issn-type":[{"value":"0960-8966","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"286th ENMC international workshop: Muscle imaging: artificial intelligence, automatic segmentation and imaging data sharing in neuromuscular disease. Hoofddorp, The Netherlands, 7-9 March 2025","name":"articletitle","label":"Article Title"},{"value":"Neuromuscular Disorders","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.nmd.2025.106304","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"106304"}}