{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:51:50Z","timestamp":1781819510071,"version":"3.54.5"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011100","name":"Spanish Foundation for Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011100","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008430","name":"Foundation for the Promotion in Asturias of Applied Scientific Research and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008430","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002810","name":"Catalan Council for Research and Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002810","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100810","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T23:41:54Z","timestamp":1777074114000},"page":"100810","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Automated 3-D carotid vessel-wall segmentation in black-blood MRI using a multilevel, context-aware deep learning approach"],"prefix":"10.1016","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4694-6120","authenticated-orcid":false,"given":"Lucas","family":"Gago","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9265-253X","authenticated-orcid":false,"given":"Beatriz","family":"Remeseiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7225-7441","authenticated-orcid":false,"given":"Laura","family":"Igual","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7855","key":"10.1016\/j.array.2026.100810_b1","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1038\/s41586-021-03392-8","article-title":"The changing landscape of atherosclerosis","volume":"592","author":"Libby","year":"2021","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.array.2026.100810_b2","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1148\/radiol.14132687","article-title":"Assessment of carotid artery atherosclerotic disease by using three-dimensional fast black-blood MR imaging: comparison with DSA","volume":"274","author":"Zhao","year":"2015","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.array.2026.100810_b3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/jmri.27399","article-title":"Black-Blood Contrast in Cardiovascular MRI","volume":"55","author":"Henningsson","year":"2022","journal-title":"J Magn Reson Imaging"},{"issue":"6","key":"10.1016\/j.array.2026.100810_b4","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1080\/10976640600777587","article-title":"Reproducibility of carotid atherosclerotic lesion type characterization using high resolution multicontrast weighted cardiovascular magnetic resonance","volume":"8","author":"Chu","year":"2006","journal-title":"J Cardiovasc Magn Reson"},{"issue":"4A","key":"10.1016\/j.array.2026.100810_b5","doi-asserted-by":"crossref","first-page":"10B","DOI":"10.1016\/S0002-9149(01)02327-X","article-title":"Ultrasound measurement of carotid plaque as a surrogate outcome for coronary artery disease","volume":"89","author":"Spence","year":"2002","journal-title":"Am J Cardiol"},{"issue":"1","key":"10.1016\/j.array.2026.100810_b6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1159\/000097034","article-title":"Mannheim Carotid Intima-Media Thickness Consensus (2004\u20132006)","volume":"23","author":"Touboul","year":"2007","journal-title":"Cerebrovasc Dis"},{"issue":"2","key":"10.1016\/j.array.2026.100810_b7","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0730-725X(98)00162-3","article-title":"Closed contour edge detection of blood vessel lumen and outer wall boundaries in black-blood MR images","volume":"17","author":"Yuan","year":"1999","journal-title":"Magn Reson Imaging"},{"key":"10.1016\/j.array.2026.100810_b8","series-title":"Medical imaging 2002: image processing","first-page":"1448","article-title":"Algorithm for quantifying advanced carotid artery atherosclerosis in humans using MRI and active contours","volume":"vol. 4684","author":"Adams","year":"2002"},{"issue":"2","key":"10.1016\/j.array.2026.100810_b9","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/jmri.20636","article-title":"Automated measurement of mean wall thickness in the common carotid artery by MRI: a comparison to intima-media thickness by B-mode ultrasound","volume":"24","author":"Underhill","year":"2006","journal-title":"J Magn Reson Imaging: An Off J Int Soc Magn Reson Med"},{"issue":"3","key":"10.1016\/j.array.2026.100810_b10","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1002\/mp.12771","article-title":"Maximization of regional probabilities using Optimal Surface Graphs: Application to carotid artery segmentation in MRI","volume":"45","author":"Arias Lorza","year":"2018","journal-title":"Med Phys"},{"issue":"1","key":"10.1016\/j.array.2026.100810_b11","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1002\/jmri.25332","article-title":"Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline","volume":"45","author":"Gao","year":"2017","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.array.2026.100810_b12","first-page":"2017","article-title":"Automatic segmentation of carotid vessel wall using convolutional neural network","volume":"96","author":"Chen","year":"2018","journal-title":"Proc Annu Meet Int Soc Magn Reson Med"},{"key":"10.1016\/j.array.2026.100810_b13","doi-asserted-by":"crossref","first-page":"217603","DOI":"10.1109\/ACCESS.2020.3040616","article-title":"Automated artery localization and vessel wall segmentation using tracklet refinement and polar conversion","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.array.2026.100810_b14","series-title":"Medical imaging 2022: image processing","first-page":"237","article-title":"Deep-learning-based carotid artery vessel wall segmentation in black-blood MRI using anatomical priors","volume":"vol. 12032","author":"Alblas","year":"2022"},{"key":"10.1016\/j.array.2026.100810_b15","series-title":"Carotid vessel wall segmentation and atherosclerosis diagnosis challenge","author":"Chen","year":"2022"},{"key":"10.1016\/j.array.2026.100810_b16","series-title":"Training dataset for carotid vessel wall segmentation and atherosclerosis diagnosis challenge, MICCAI 2022","author":"2022","year":"2022"},{"key":"10.1016\/j.array.2026.100810_b17","series-title":"Testing dataset for carotid vessel wall segmentation and atherosclerosis diagnosis challenge, MICCAI 2022","author":"2022","year":"2022"},{"key":"10.1016\/j.array.2026.100810_b18","series-title":"Label propagation for 3D carotid vessel wall segmentation and atherosclerosis diagnosis","author":"Hu","year":"2022"},{"issue":"2","key":"10.1016\/j.array.2026.100810_b19","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":"Nature Methods"},{"key":"10.1016\/j.array.2026.100810_b20","series-title":"DBF-UNet: A Two-Stage Framework for Carotid Artery Segmentation with Pseudo-Label Generation","author":"Li","year":"2025"},{"key":"10.1016\/j.array.2026.100810_b21","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y, et al. Segment anything. In: IEEE\/CVF international conference on computer vision. 2023, p. 4015\u201326.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.array.2026.100810_b22","series-title":"SAM-Med2D","author":"Cheng","year":"2023"},{"issue":"1","key":"10.1016\/j.array.2026.100810_b23","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat Commun"},{"issue":"1","key":"10.1016\/j.array.2026.100810_b24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"10.1016\/j.array.2026.100810_b25","series-title":"Deep learning in medical image analysis and multimodal learning for clinical decision support","first-page":"3","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.array.2026.100810_b26","unstructured":"Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In: International conference on machine learning. 2019, p. 6105\u201314."},{"key":"10.1016\/j.array.2026.100810_b27","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. 2009, p. 248\u201355.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10.1016\/j.array.2026.100810_b28","unstructured":"Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: 32nd international conference on machine learning. Vol. 37, 2015, p. 448\u201356."},{"key":"10.1016\/j.array.2026.100810_b29","series-title":"Deep Learning using Rectified Linear Units (ReLU)","author":"Agarap","year":"2018"},{"key":"10.1016\/j.array.2026.100810_b30","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. 2015, p. 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.array.2026.100810_b31","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal Loss for Dense Object Detection. In: IEEE international conference on computer vision. 2017, p. 2999\u20133007.","DOI":"10.1109\/ICCV.2017.324"},{"issue":"5","key":"10.1016\/j.array.2026.100810_b32","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1097\/rmr.0b013e3181598d9d","article-title":"Magnetic resonance imaging of carotid atherosclerosis: plaque analysis","volume":"18","author":"Kerwin","year":"2007","journal-title":"Top Magn Reson Imaging"},{"issue":"7","key":"10.1016\/j.array.2026.100810_b33","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1056\/NEJM199108153250701","article-title":"Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis","volume":"325","author":"North American Symptomatic Carotid Endarterectomy Trial Collaborators","year":"1991","journal-title":"New Engl J Med"},{"key":"10.1016\/j.array.2026.100810_b34","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.array.2026.100810_b35","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. In: 3rd international conference on learning representations. 2015, p. 1\u201315."},{"issue":"1","key":"10.1016\/j.array.2026.100810_b36","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1161\/01.ATV.0000149867.61851.31","article-title":"Quantitative evaluation of carotid plaque composition by in vivo MRI","volume":"25","author":"Saam","year":"2005","journal-title":"Arterioscler Thromb Vasc Biol"},{"issue":"4","key":"10.1016\/j.array.2026.100810_b37","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1161\/CIRCULATIONAHA.106.628875","article-title":"Prediction of clinical cardiovascular events with carotid intima-media thickness: a systematic review and meta-analysis","volume":"115","author":"Lorenz","year":"2007","journal-title":"Circulation"},{"issue":"12","key":"10.1016\/j.array.2026.100810_b38","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.recesp.2012.04.026","article-title":"Carotid Intima-media Thickness in the Spanish Population: Reference Ranges and Association With Cardiovascular Risk Factors","volume":"65","author":"Grau","year":"2012","journal-title":"Rev Espa\u00d1ola de Cardiol"},{"issue":"4","key":"10.1016\/j.array.2026.100810_b39","first-page":"290","article-title":"Mannheim carotid intima-media thickness and plaque consensus (2004\u20132006-2011)","volume":"34","author":"Touboul","year":"2012","journal-title":"Cardiovasc Dis"},{"issue":"2","key":"10.1016\/j.array.2026.100810_b40","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1002\/jmri.22043","article-title":"MRI measurements of carotid plaque in the atherosclerosis risk in communities (ARIC) study: methods, reliability and descriptive statistics","volume":"31","author":"Wasserman","year":"2010","journal-title":"J Magn Reson Imaging: An Off J Int Soc Magn Reson Med"},{"issue":"8476","key":"10.1016\/j.array.2026.100810_b41","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical methods for assessing agreement between two methods of clinical measurement","volume":"1","author":"Bland","year":"1986","journal-title":"Lancet"},{"issue":"4","key":"10.1016\/j.array.2026.100810_b42","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.11.4.044503","article-title":"Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data","volume":"11","author":"Rahlfs","year":"2024","journal-title":"J Med Imaging"},{"key":"10.1016\/j.array.2026.100810_b43","series-title":"International conference on medical image computing and computer-assisted intervention","article-title":"Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation","author":"Gotkowski","year":"2025"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001335?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001335?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T20:57:34Z","timestamp":1781816254000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626001335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":43,"alternative-id":["S2590005626001335"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100810","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated 3-D carotid vessel-wall segmentation in black-blood MRI using a multilevel, context-aware deep learning approach","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100810","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100810"}}