{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:00:10Z","timestamp":1786114810321,"version":"build-2736575974"},"reference-count":28,"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":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100006988","name":"John S. Dunn Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006988","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.compmedimag.2026.102792","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:12:54Z","timestamp":1782573174000},"page":"102792","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT"],"prefix":"10.1016","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4474-6683","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Bagchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John Julius","family":"Volpi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timea","family":"Hodics","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen T.C.","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8329-6123","authenticated-orcid":false,"given":"Kelvin","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2026.102792_b1","series-title":"Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation","author":"Alom","year":"2018"},{"issue":"5","key":"10.1016\/j.compmedimag.2026.102792_b2","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190183","article-title":"Fully automated segmentation of head CT neuroanatomy using deep learning","volume":"2","author":"Cai","year":"2020","journal-title":"Radiol. Artif. Intell."},{"issue":"6","key":"10.1016\/j.compmedimag.2026.102792_b3","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.21037\/qims-19-1090","article-title":"Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network","volume":"10","author":"Cai","year":"2020","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.compmedimag.2026.102792_b4","series-title":"European Conference on Computer Vision","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102792_b5","series-title":"TransUNet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102792_b6","doi-asserted-by":"crossref","unstructured":"Chidester, B., Ton, T.-V., Tran, M.-T., Ma, J., Do, M.N., 2019. Enhanced Rotation-Equivariant U-Net for Nuclear Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.","DOI":"10.1109\/CVPRW.2019.00143"},{"key":"10.1016\/j.compmedimag.2026.102792_b7","series-title":"Proceedings of the 33rd International Conference on Machine Learning","first-page":"2990","article-title":"Group equivariant convolutional networks","volume":"vol. 48","author":"Cohen","year":"2016"},{"issue":"7","key":"10.1016\/j.compmedimag.2026.102792_b8","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1111\/jth.15336","article-title":"Thrombolytic strategies for ischemic stroke in the thrombectomy era","volume":"19","author":"Gauberti","year":"2021","journal-title":"J. Thromb. Haemost."},{"key":"10.1016\/j.compmedimag.2026.102792_b9","series-title":"Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102792_b10","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Yang, D., Roth, H.R., Xu, D., 2021. UNETR: Transformers for 3D Medical Image Segmentation. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision. WACV, pp. 1748\u20131758.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10.1016\/j.compmedimag.2026.102792_b11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fninf.2019.00009","article-title":"Brain segmentation from computed tomography of healthy aging and geriatric concussion at variable spatial resolutions","volume":"13","author":"Irimia","year":"2019","journal-title":"Front. Neuroinformatics"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102792_b12","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"},{"issue":"13","key":"10.1016\/j.compmedimag.2026.102792_b13","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1212\/WNL.0b013e3181b9c847","article-title":"Good clinical outcome after ischemic stroke with successful revascularization is time-dependent","volume":"73","author":"Khatri","year":"2009","journal-title":"Neurology"},{"issue":"6","key":"10.1016\/j.compmedimag.2026.102792_b14","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.jcct.2009.07.003","article-title":"What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT?","volume":"3","author":"Lin","year":"2009","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"10.1016\/j.compmedimag.2026.102792_b15","series-title":"Proceedings of the First International Workshop on Geometric Deep Learning in Medical Image Analysis","first-page":"96","article-title":"Group convolutional neural networks for DWI segmentation","volume":"vol. 194","author":"Liu","year":"2022"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102792_b16","article-title":"Detection of early ischemic changes in noncontrast CT head improved with \u201cstroke windows\u201d","volume":"2014","author":"Mainali","year":"2014","journal-title":"Int. Sch. Res. Not."},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102792_b17","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1161\/STROKEAHA.113.003798","article-title":"Acute ischemic stroke","volume":"45","author":"Manning","year":"2014","journal-title":"Stroke"},{"key":"10.1016\/j.compmedimag.2026.102792_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105711","article-title":"Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model","volume":"196","author":"Nishio","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compmedimag.2026.102792_b19","series-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"23","key":"10.1016\/j.compmedimag.2026.102792_b20","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.1001\/jama.2013.6959","article-title":"Time to treatment with intravenous tissue plasminogen activator and outcome from acute ischemic stroke","volume":"309","author":"Saver","year":"2013","journal-title":"JAMA"},{"key":"10.1016\/j.compmedimag.2026.102792_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2021.118606","article-title":"Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT","volume":"244","author":"Srikrishna","year":"2021","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102792_b22","doi-asserted-by":"crossref","first-page":"161","DOI":"10.7861\/clinmedicine.17-2-161","article-title":"Thrombolysis and thrombectomy for acute ischaemic stroke","volume":"17","author":"Tawil","year":"2017","journal-title":"Clin. Med."},{"issue":"7","key":"10.1016\/j.compmedimag.2026.102792_b23","doi-asserted-by":"crossref","first-page":"4664","DOI":"10.1002\/mp.16529","article-title":"SynthRAD2023 grand challenge dataset: Generating synthetic CT for radiotherapy","volume":"50","author":"Thummerer","year":"2023","journal-title":"Medical Physics"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102792_b24","doi-asserted-by":"crossref","DOI":"10.3390\/fractalfract10010018","article-title":"When RNN meets CNN and ViT: The development of a hybrid U-Net for medical image segmentation","volume":"10","author":"Wang","year":"2026","journal-title":"Fractal Fract."},{"issue":"3","key":"10.1016\/j.compmedimag.2026.102792_b25","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1016\/j.neuroimage.2010.10.023","article-title":"Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT)","volume":"54","author":"Weiskopf","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2026.102792_b26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.media.2019.03.010","article-title":"Pulmonary nodule detection in CT scans with equivariant CNNs","volume":"55","author":"Winkels","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2026.102792_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101996","article-title":"Retrospective study of deep learning to reduce noise in non-contrast head CT images","volume":"94","author":"Wong","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"issue":"9","key":"10.1016\/j.compmedimag.2026.102792_b28","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.1161\/STROKEAHA.121.037982","article-title":"Automatic segmentation in acute ischemic stroke: Prognostic significance of topological stroke volumes on stroke outcome","volume":"53","author":"Wong","year":"2022","journal-title":"Stroke"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000959?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000959?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T21:35:43Z","timestamp":1785965743000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611126000959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":28,"alternative-id":["S0895611126000959"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102792","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102792","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102792"}}