{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:24:57Z","timestamp":1783023897791,"version":"3.54.6"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006683","name":"Xi\u2019an Jiaotong-Liverpool University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006683","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110792","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:25:47Z","timestamp":1781688347000},"page":"110792","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["RCDAN: A novel network for retinal vessel segmentation with rotational convolution and dynamic attention"],"prefix":"10.1016","volume":"125","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2642-598X","authenticated-orcid":false,"given":"Guojie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3094-5596","authenticated-orcid":false,"given":"Anwar P.P. Abdul","family":"Majeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9335-817X","authenticated-orcid":false,"given":"Muhammad","family":"Ateeq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1449-211X","authenticated-orcid":false,"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6228-940X","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.bspc.2026.110792_b1","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1007\/s10439-022-03058-0","article-title":"Retinal vessel segmentation, a review of classic and deep methods","volume":"50","author":"Khandouzi","year":"2022","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110792_b2","series-title":"2020 IEEE International Conference on Systems, Man, and Cybernetics","first-page":"3142","article-title":"A multi-task framework for topology-guaranteed retinal layer segmentation in oct images","author":"Cao","year":"2020"},{"issue":"5","key":"10.1016\/j.bspc.2026.110792_b3","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.3390\/diagnostics12051234","article-title":"Cardiovascular risk stratification in diabetic retinopathy via atherosclerotic pathway in COVID-19\/non-COVID-19 frameworks using artificial intelligence paradigm: a narrative review","volume":"12","author":"Munjral","year":"2022","journal-title":"Diagnostics"},{"issue":"2","key":"10.1016\/j.bspc.2026.110792_b4","doi-asserted-by":"crossref","first-page":"H201","DOI":"10.1152\/ajpheart.00201.2016","article-title":"Retinal microvascular network alterations: potential biomarkers of cerebrovascular and neural diseases","volume":"312","author":"Cabrera DeBuc","year":"2017","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"10.1016\/j.bspc.2026.110792_b5","doi-asserted-by":"crossref","first-page":"57796","DOI":"10.1109\/ACCESS.2022.3178372","article-title":"A comprehensive review of deep learning strategies in retinal disease diagnosis using fundus images","volume":"10","author":"Goutam","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110792_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126626","article-title":"A comprehensive survey on segmentation techniques for retinal vessel segmentation","volume":"556","author":"Cervantes","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.110792_b7","doi-asserted-by":"crossref","unstructured":"X. Chen, Y. Yuan, G. Zeng, J. Wang, Semi-supervised semantic segmentation with cross pseudo supervision, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2613\u20132622.","DOI":"10.1109\/CVPR46437.2021.00264"},{"issue":"11","key":"10.1016\/j.bspc.2026.110792_b8","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.bspc.2026.110792_b9","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.bspc.2026.110792_b10","series-title":"Advances in Robotics, Automation and Data Analytics: Selected Papers from ICITES 2020","first-page":"391","article-title":"The diagnosis of diabetic retinopathy: a transfer learning with support vector machine approach","author":"Noor","year":"2021"},{"key":"10.1016\/j.bspc.2026.110792_b11","doi-asserted-by":"crossref","first-page":"41180","DOI":"10.1109\/ACCESS.2024.3376441","article-title":"A comprehensive survey of convolutions in deep learning: Applications, challenges, and future trends","volume":"12","author":"Younesi","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110792_b12","doi-asserted-by":"crossref","unstructured":"Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A convnet for the 2020s, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.bspc.2026.110792_b13","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5672","article-title":"InceptionNeXt: When inception meets ConvNeXt","author":"Yu","year":"2024"},{"issue":"10s","key":"10.1016\/j.bspc.2026.110792_b14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.bspc.2026.110792_b15","series-title":"International Conference on Robot Intelligence Technology and Applications","first-page":"95","article-title":"Shape-sensitive loss for catheter and guidewire segmentation","author":"Kongtongvattana","year":"2023"},{"issue":"3","key":"10.1016\/j.bspc.2026.110792_b16","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.icte.2026.02.006","article-title":"MA-KANet: Enhancing retinal vessel segmentation through multi-scale feature fusion and attention mechanisms","volume":"12","author":"Li","year":"2026","journal-title":"ICT Express"},{"key":"10.1016\/j.bspc.2026.110792_b17","doi-asserted-by":"crossref","unstructured":"S. Yun, Y. Ro, Shvit: Single-head vision transformer with memory efficient macro design, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 5756\u20135767.","DOI":"10.1109\/CVPR52733.2024.00550"},{"key":"10.1016\/j.bspc.2026.110792_b18","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10.1016\/j.bspc.2026.110792_b19","doi-asserted-by":"crossref","unstructured":"S. Woo, J. Park, J.-Y. Lee, I.S. Kweon, Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.bspc.2026.110792_b20","series-title":"European Conference on Computer Vision","first-page":"56","article-title":"Efficient image super-resolution using pixel attention","author":"Zhao","year":"2020"},{"key":"10.1016\/j.bspc.2026.110792_b21","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","article-title":"DUNet: A deformable network for retinal vessel segmentation","volume":"178","author":"Jin","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110792_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106595","article-title":"ARSA-UNet: Atrous residual network based on structure-adaptive model for retinal vessel segmentation","volume":"96","author":"Xie","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110792_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108047","article-title":"CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation","volume":"170","author":"Jiang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110792_b24","first-page":"1","article-title":"Diffusion probabilistic learning with gate-fusion transformer and edge-frequency attention for retinal vessel segmentation","volume":"73","author":"Li","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.bspc.2026.110792_b25","series-title":"2017 IEEE International Conference on Computer Vision","first-page":"764","article-title":"Deformable convolutional networks","author":"Dai","year":"2017"},{"key":"10.1016\/j.bspc.2026.110792_b26","series-title":"2023 IEEE\/CVF International Conference on Computer Vision","first-page":"6047","article-title":"Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation","author":"Qi","year":"2023"},{"issue":"5","key":"10.1016\/j.bspc.2026.110792_b27","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.immuni.2009.09.009","article-title":"Immune and nervous systems: more than just a superficial similarity?","volume":"31","author":"Kioussis","year":"2009","journal-title":"Immunity"},{"key":"10.1016\/j.bspc.2026.110792_b28","series-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.bspc.2026.110792_b29","doi-asserted-by":"crossref","unstructured":"F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.1016\/j.bspc.2026.110792_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106731","article-title":"DSML-UNet: Depthwise separable convolution network with multiscale large kernel for medical image segmentation","volume":"97","author":"Wang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"10.1016\/j.bspc.2026.110792_b31","doi-asserted-by":"crossref","first-page":"17324","DOI":"10.48084\/etasr.8484","article-title":"A multi-head self-attention mechanism for improved brain tumor classification using deep learning approaches","volume":"14","author":"Reddi","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"issue":"6","key":"10.1016\/j.bspc.2026.110792_b32","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TRPMS.2023.3265863","article-title":"Current and emerging trends in medical image segmentation with deep learning","volume":"7","author":"Conze","year":"2023","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"issue":"4","key":"10.1016\/j.bspc.2026.110792_b33","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"10.1016\/j.bspc.2026.110792_b34","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","article-title":"An ensemble classification-based approach applied to retinal blood vessel segmentation","volume":"59","author":"Fraz","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"10.1016\/j.bspc.2026.110792_b35","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/42.845178","article-title":"Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response","volume":"19","author":"Hoover","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110792_b36","series-title":"ICCV","first-page":"6023","article-title":"CutMix: Regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"key":"10.1016\/j.bspc.2026.110792_b37","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","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.bspc.2026.110792_b38","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.bspc.2026.110792_b39","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.3389\/fgene.2019.01110","article-title":"Channel-Unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation","volume":"10","author":"Chen","year":"2019","journal-title":"Front. Genet."},{"key":"10.1016\/j.bspc.2026.110792_b40","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.bspc.2026.110792_b41","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110792_b42","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110792_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109512","article-title":"ConvUNeXt: An efficient convolution neural network for medical image segmentation","volume":"253","author":"Han","year":"2022","journal-title":"Knowl.-Based Syst."},{"issue":"9","key":"10.1016\/j.bspc.2026.110792_b44","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1109\/JBHI.2022.3188710","article-title":"Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110792_b45","series-title":"2024 IEEE International Symposium on Biomedical Imaging","first-page":"1","article-title":"CMUNeXt: An efficient medical image segmentation network based on large kernel and skip fusion","author":"Tang","year":"2024"},{"key":"10.1016\/j.bspc.2026.110792_b46","doi-asserted-by":"crossref","unstructured":"C. Li, X. Liu, W. Li, C. Wang, H. Liu, Y. Liu, Z. Chen, Y. Yuan, U-kan makes strong backbone for medical image segmentation and generation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, (5) 2025, pp. 4652\u20134660.","DOI":"10.1609\/aaai.v39i5.32491"},{"key":"10.1016\/j.bspc.2026.110792_b47","series-title":"Full-scale representation guided network for retinal vessel segmentation","author":"Seo","year":"2025"},{"issue":"7","key":"10.1016\/j.bspc.2026.110792_b48","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1038\/s41433-018-0032-4","article-title":"Diabetic retinopathy and endothelin system: microangiopathy versus endothelial dysfunction","volume":"32","author":"Sorrentino","year":"2018","journal-title":"Eye"},{"issue":"7","key":"10.1016\/j.bspc.2026.110792_b49","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1038\/eye.2009.108","article-title":"Microvascular lesions of diabetic retinopathy: clues towards understanding pathogenesis?","volume":"23","author":"Curtis","year":"2009","journal-title":"Eye"},{"key":"10.1016\/j.bspc.2026.110792_b50","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"11","key":"10.1016\/j.bspc.2026.110792_b51","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on explainable artificial intelligence (xai): Toward medical xai","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.bspc.2026.110792_b52","article-title":"Sanity checks for saliency maps","volume":"31","author":"Adebayo","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013467?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013467?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:56:29Z","timestamp":1783022189000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426013467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":52,"alternative-id":["S1746809426013467"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110792","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RCDAN: A novel network for retinal vessel segmentation with rotational convolution and dynamic attention","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110792","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":"110792"}}