{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T14:26:48Z","timestamp":1790432808773,"version":"4.1.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001634","name":"University of Galway","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001634","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["SFI\/12\/RC\/2289_P2"],"award-info":[{"award-number":["SFI\/12\/RC\/2289_P2"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.compmedimag.2024.102460","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T07:47:42Z","timestamp":1731916062000},"page":"102460","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Exploratory analysis of Type B Aortic Dissection (TBAD) segmentation in 2D CTA images using various kernels"],"prefix":"10.1016","volume":"118","author":[{"given":"Ayman","family":"Abaid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivas","family":"Ilancheran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9505-2732","authenticated-orcid":false,"given":"Talha","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niamh","family":"Hynes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7964-5199","authenticated-orcid":false,"given":"Ihsan","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.compmedimag.2024.102460_b1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s40537-023-00727-2","article-title":"A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications","volume":"10","author":"Alzubaidi","year":"2023","journal-title":"J. Big Data"},{"key":"10.1016\/j.compmedimag.2024.102460_b2","doi-asserted-by":"crossref","first-page":"226,","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical image analysis using convolutional neural networks: A review","volume":"42","author":"Anwar","year":"2018","journal-title":"J. Med. Syst."},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102460_b3","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/JBHI.2013.2269292","article-title":"Segmentation of the thoracic aorta in noncontrast cardiac CT images","volume":"17","author":"Avila-Montes","year":"2013","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.compmedimag.2024.102460_b4","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: The success of u-net","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compmedimag.2024.102460_b5","series-title":"Universeg: Universal medical image segmentation","author":"Butoi","year":"2023"},{"key":"10.1016\/j.compmedimag.2024.102460_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2019.108713","article-title":"Fully automatic segmentation of type b aortic dissection from cta images enabled by deep learning","volume":"121","author":"Cao","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.compmedimag.2024.102460_b7","article-title":"Deep neural networks segment neuronal membranes in electron microscopy images","volume":"25","author":"Ciresan","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2024.102460_b8","series-title":"2017 IEEE 14th International Symposium on Biomedical Imaging","first-page":"557","article-title":"Automatic detection of aortic dissection in contrast-enhanced CT","author":"Dehghan","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102460_b9","series-title":"Computer Vision \u2013 ECCV 2018 Workshops","first-page":"607","article-title":"EmoP3D: A brain like pyramidal deep neural network for emotion recognition","author":"Di Nardo","year":"2019"},{"issue":"17","key":"10.1016\/j.compmedimag.2024.102460_b10","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1161\/CIRCULATIONAHA.117.031264","article-title":"Insights from the international registry of acute aortic dissection","volume":"137","author":"Evangelista","year":"2018","journal-title":"Circulation"},{"issue":"17","key":"10.1016\/j.compmedimag.2024.102460_b11","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1161\/CIRCULATIONAHA.117.031264","article-title":"Insights from the international registry of acute aortic dissection: a 20-year experience of collaborative clinical research","volume":"137","author":"Evangelista","year":"2018","journal-title":"Circulation"},{"key":"10.1016\/j.compmedimag.2024.102460_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102189","article-title":"Fusing 2D and 3D convolutional neural networks for the segmentation of aorta and coronary arteries from CT images","volume":"121","author":"Gu","year":"2021","journal-title":"Artif. Intell. Med."},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102460_b13","article-title":"CT-based true-and false-lumen segmentation in type b aortic dissection using machine learning","volume":"2","author":"Hahn","year":"2020","journal-title":"Radiol.: Cardiothorac. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102460_b14","series-title":"Facebookresearch\/segment-anything: The repository provides code for running inference with the segmentanything model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model","author":"HannaMao","year":"2023"},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102460_b15","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.amjcard.2006.10.055","article-title":"Frequency of and inappropriate treatment of misdiagnosis of acute aortic dissection","volume":"99","author":"Hansen","year":"2007","journal-title":"Am. J. Cardiol."},{"issue":"11","key":"10.1016\/j.compmedimag.2024.102460_b16","doi-asserted-by":"crossref","first-page":"16591","DOI":"10.1007\/s11042-022-13820-0","article-title":"The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study","volume":"82","author":"Hassan","year":"2023","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compmedimag.2024.102460_b17","doi-asserted-by":"crossref","unstructured":"He, K., 2016. Identity mappings in deep residual networks\u201d. In: Proceedings of ECCV. pp. 630\u2013645.","DOI":"10.1007\/978-3-319-46493-0_38"},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102460_b18","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1016\/j.jvs.2018.04.034","article-title":"Favorable impact of thoracic endovascular aortic repair on survival of patients with acute uncomplicated type b aortic dissection","volume":"68","author":"Iannuzzi","year":"2018","journal-title":"J. Vasc. Surg."},{"key":"10.1016\/j.compmedimag.2024.102460_b19","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","first-page":"234","article-title":"No new-net","author":"Isensee","year":"2019"},{"key":"10.1016\/j.compmedimag.2024.102460_b20","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1177\/1538574410362118","article-title":"The diagnosis and management of aortic dissection","volume":"44","author":"Karthikesalingam","year":"2010","journal-title":"Vasc. Endovascular Surg."},{"key":"10.1016\/j.compmedimag.2024.102460_b21","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.compmedimag.2024.102460_b22","series-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.compmedimag.2024.102460_b23","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T., 2015. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10.1016\/j.compmedimag.2024.102460_b24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ajem.2021.11.047","article-title":"Misdiagnosis of aortic dissection: A systematic review of the literature","volume":"53","author":"Lovatt","year":"2022","journal-title":"Am. J. Emerg. Med."},{"key":"10.1016\/j.compmedimag.2024.102460_b25","series-title":"Seminars in Roentgenology","first-page":"357","article-title":"Artificial intelligence applications in aortic dissection imaging","volume":"Vol. 57","author":"Mastrodicasa","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102460_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: an experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"issue":"24","key":"10.1016\/j.compmedimag.2024.102460_b27","first-page":"e223","article-title":"2022 ACC\/AHA guideline for the diagnosis and management of aortic disease: a report of the American heart association\/American college of cardiology joint committee on clinical practice guidelines","volume":"80","author":"Members","year":"2022","journal-title":"J. Am. Coll. Cardiol."},{"issue":"18","key":"10.1016\/j.compmedimag.2024.102460_b28","doi-asserted-by":"crossref","first-page":"5963","DOI":"10.3390\/jcm12185963","article-title":"The role of spatial aortic arch architecture in type B aortic dissection","volume":"12","author":"Mulorz","year":"2023","journal-title":"J. Clin. Med."},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102460_b29","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ejvs.2020.04.005","article-title":"Surgical decision making in uncomplicated type b aortic dissection: a survey of Australian\/New Zealand and European surgeons","volume":"60","author":"Munshi","year":"2020","journal-title":"Eur. J. Vasc. Endovascular Surg."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102460_b30","first-page":"1","article-title":"Aortic dissection","volume":"2","author":"Nienaber","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"issue":"25","key":"10.1016\/j.compmedimag.2024.102460_b31","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1161\/CIRCULATIONAHA.122.061065","article-title":"Clinical characteristics, incidences, and mortality rates for type a and b aortic dissections: A nationwide danish population-based cohort study from 1996 to 2016","volume":"146","author":"Obel","year":"2022","journal-title":"Circulation"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102460_b32","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jacc.2015.05.029","article-title":"Presentation, diagnosis, and outcomes of acute aortic dissection: 17-year trends from the international registry of acute aortic dissection","volume":"66","author":"Pape","year":"2015","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.compmedimag.2024.102460_b33","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation\u201d","author":"Ronneberger","year":"2015"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102460_b34","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.jtcvs.2012.11.048","article-title":"Importance of false lumen thrombosis in type B aortic dissection prognosis","volume":"145","author":"Trimarchi","year":"2013","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"10.1016\/j.compmedimag.2024.102460_b35","series-title":"Image Analysis and Processing\u2014ICIAP 2015: 18th International Conference, Genoa, Italy, September 7-11, 2015, Proceedings, Part I 18","first-page":"236","article-title":"A strict pyramidal deep neural network for action recognition","author":"Ullah","year":"2015"},{"key":"10.1016\/j.compmedimag.2024.102460_b36","series-title":"Pattern Recognition and Machine Intelligence","first-page":"591","article-title":"A spatio-temporal feature learning approach for dynamic scene recognition","author":"Ullah","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102460_b37","doi-asserted-by":"crossref","first-page":"105891,","DOI":"10.1016\/j.compbiomed.2022.105891","article-title":"Stacked dilated convolutions and asymmetric architecture for U-net-based medical image segmentation","volume":"148","author":"Wang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2024.102460_b38","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"240","article-title":"Deep learning-based 3D segmentation of true lumen, false lumen, and false lumen thrombosis in type-b aortic dissection","author":"Wobben","year":"2021"},{"key":"10.1016\/j.compmedimag.2024.102460_b39","series-title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","author":"Wu","year":"2023"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102460_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2023.100727","article-title":"ADSeg: A flap-attention-based deep learning approach for aortic dissection segmentation","volume":"4","author":"Xiang","year":"2023","journal-title":"Patterns"},{"key":"10.1016\/j.compmedimag.2024.102460_b41","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2021.732711","article-title":"ImageTBAD: A 3D computed tomography angiography image dataset for automatic segmentation of type-B aortic dissection","volume":"12","author":"Yao","year":"2021","journal-title":"Front. Physiol."},{"key":"10.1016\/j.compmedimag.2024.102460_b42","unstructured":"Yu, F., Koltun, V., 2016. Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations."},{"key":"10.1016\/j.compmedimag.2024.102460_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102219","article-title":"Semi-supervised aortic dissections segmentation: A time-dependent weighted feedback fusion framework","volume":"106","author":"Zhang","year":"2023","journal-title":"Comput. Med. Imaging Graph."}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089561112400137X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089561112400137X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T14:32:02Z","timestamp":1741271522000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S089561112400137X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":43,"alternative-id":["S089561112400137X"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102460","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Exploratory analysis of Type B Aortic Dissection (TBAD) segmentation in 2D CTA images using various kernels","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102460","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102460"}}