{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:06:33Z","timestamp":1779275193799,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Application Demonstration of Key Technologies for Multimodal Intelligent Diagnosis and Treatment Auxiliary Decision Support System","award":["2025TSGCCZZB0610"],"award-info":[{"award-number":["2025TSGCCZZB0610"]}]},{"name":"Research and Application Demonstration of Key Technologies for Multimodal Intelligent Diagnosis and Treatment Auxiliary Decision Support System","award":["2025TS1087"],"award-info":[{"award-number":["2025TS1087"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate coronary artery segmentation is essential for diagnosis and interventional planning, but conventional U-shaped networks often miss thin, low-contrast vessels and break vessel continuity. We propose Inverted Pyramid-Shaped Multi-resolution U-Net (IPSM-UNet), a dual U-Net++ architecture with multi-resolution feature interaction, feature aggregation, and layer-wise deep supervision. The method is evaluated on DRIVE, CHASE_DB1, DCA1, and an internal coronary angiography dataset. IPSM-UNet achieves competitive or better performance across datasets, including F1 = 0.8310 and Acc = 0.9707 on DRIVE, Se = 0.8792 and Acc = 0.9745 on CHASE_DB1, F1 = 0.8043 and Acc = 0.9793 on DCA1, and Se = 0.8741, F1 = 0.8590, and Acc = 0.9879 on the internal dataset. IPSM-UNet improves vessel continuity and overall segmentation quality, particularly for small-caliber vessels, and supports downstream coronary analysis.<\/jats:p>","DOI":"10.3390\/jimaging12050216","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T09:12:41Z","timestamp":1779268361000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IPSM-UNet: An Inverted Pyramid-Shaped U-Net++ Architecture with Multi-Resolution Information Interaction for Coronary Artery Segmentation"],"prefix":"10.3390","volume":"12","author":[{"given":"Yinong","family":"Liao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese PLA General Hospital Medical School, Beijing 100853, China"},{"name":"Department of Adult Cardiac Surgery, Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guopeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Adult Cardiac Surgery, Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Adult Cardiac Surgery, Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6037","DOI":"10.1007\/s00330-022-08761-z","article-title":"Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography","volume":"32","author":"Li","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hogea, T., Suciu, B.A., Iv\u0103nescu, A.D., Cara\u0219ca, C., Chinezu, L., Arb\u0103na\u0219i, E.M., Russu, E., Kaller, R., Arb\u0103na\u0219i, E.M., and Mure\u0219an, A.V. (2023). Increased epicardial adipose tissue (EAT), left coronary artery plaque morphology, and valvular atherosclerosis as risks factors for sudden cardiac death from a forensic perspective. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010142"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Singh, N.P., Kumar, R., and Srivastava, R. (2015, January 15\u201316). Local entropy thresh- olding based fast retinal vessels segmentation by modifying matched filter. Proceedings of the International Conference on Computing, Communication & Automation, Greater Noida, India.","DOI":"10.1109\/CCAA.2015.7148552"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2016.10.015","article-title":"Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation","volume":"139","author":"Javidi","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101702","DOI":"10.1016\/j.artmed.2019.07.010","article-title":"Retinal image assessment using bi-level adaptive morphological component analysis","volume":"99","author":"Javidi","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101623","DOI":"10.1016\/j.media.2019.101623","article-title":"Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmen-tation","volume":"60","author":"Wang","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.cmpb.2017.08.018","article-title":"Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model","volume":"151","author":"Kalaie","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_9","unstructured":"Hoffman, J., Wang, D., Yu, F., and Darrell, T. (2016). Fcns in the wild: Pixel-level adversarial and constraint-based adaptation. arXiv."},{"key":"ref_10","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). U-Net++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018, Springer International Publishing."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"ZIbtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_12","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":"ref_13","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":"ref_14","doi-asserted-by":"crossref","first-page":"84040","DOI":"10.1109\/ACCESS.2019.2924744","article-title":"Dual U-Net for the segmentation of overlapping glioma nuclei","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1080\/0954898X.2023.2288858","article-title":"CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation","volume":"35","author":"Yang","year":"2024","journal-title":"Netw. Comput. Neural Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103496","DOI":"10.1016\/j.media.2025.103496","article-title":"Deep learning based coronary vessels segmentation in X-ray angiography using temporal information","volume":"102","author":"He","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"016002","DOI":"10.1117\/1.JMI.12.1.016002","article-title":"Improving coronary artery segmentation with self-supervised learning and automated pericoronary adipose tissue segmentation: A multi-institutional study on coronary computed tomography angiography images","volume":"12","author":"Kim","year":"2025","journal-title":"J. Med. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, R., Dai, L., Wang, J., Zhang, L., and Wang, Y. (2025). SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model. J. Imaging, 11.","DOI":"10.3390\/jimaging11060192"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ramos-Cortez, J.S., Alvarado-Carrillo, D.E., Ovalle-Magallanes, E., and Avina-Cervantes, J.G. (2025). Lightweight U-Net for Blood Vessels Segmentation in X-Ray Coronary Angiography. J. Imaging, 11.","DOI":"10.3390\/jimaging11040106"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, Y., Lyu, Z., Mai, Y., Li, S., and Hu, C. (2025). VM-CAGSeg: A vessel structure-aware state space model for coronary artery segmentation in angiography images. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1661680"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"41096","DOI":"10.1038\/s41598-025-24953-1","article-title":"Seg2RefineNet: A novel DL-based framework for 2D CCTA image-based segmentation and 3D volume-based refinement","volume":"15","author":"Khan","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wei, G., Zeng, X., and Zhang, Q. (J. Imaging Inform. Med., 2025). FlowVM-Net: Enhanced Vessel Segmentation in X-Ray Coronary Angiography Using Temporal Information Fusion, J. Imaging Inform. Med., online ahead of print.","DOI":"10.1007\/s10278-025-01732-y"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109172","DOI":"10.1016\/j.cmpb.2025.109172","article-title":"CoroSAM: Adaptation of the Segment Anything Model for interactive segmentation in Coronary angiograms","volume":"274","author":"Ferrari","year":"2026","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105534","DOI":"10.1016\/j.bspc.2023.105534","article-title":"HiFuse: Hierarchical multi-scale feature fusion network for medical image classification","volume":"87","author":"Huo","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1049\/sil2.12114","article-title":"FFUNet: A novel feature fusion makes strong decoder for medical image segmentation","volume":"16","author":"Xie","year":"2022","journal-title":"IET Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lei, M., Wu, H., Lv, X., and Wang, X. (2024). ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature Enhancement. arXiv.","DOI":"10.1609\/aaai.v39i5.32482"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111072","DOI":"10.1016\/j.patcog.2024.111072","article-title":"CEDNet: A cascade encoder\u2013decoder network for dense prediction","volume":"158","author":"Zhang","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, X., Jia, H., Pang, Y., Lv, L., Tian, F., Zhang, L., Sun, W., and Lu, H. (2023). M2 SNet: Multi-scale in multi-scale subtraction network for medical image segmentation. arXiv.","DOI":"10.1016\/j.bspc.2023.105330"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, A., Hu, X., Hu, C., Xu, J., and Zhou, S. (2024). Scale-adaptive feature aggregation for efficient space-time video super-resolution. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 3\u20138 January 2024, IEEE.","DOI":"10.1109\/WACV57701.2024.00418"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/TMI.2022.3161681","article-title":"Retinal vessel segmentation with skeletal prior and contrastive loss","volume":"41","author":"Tan","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105980","DOI":"10.1016\/j.bspc.2024.105980","article-title":"IMFF-Net: An integrated multi-scale feature fusion network for accurate retinal vessel segmentation from fundus images","volume":"91","author":"Liu","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_32","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"Volume 37","author":"Bach","year":"2015","journal-title":"Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 7\u20139 July 2015"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","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"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2018.06.007","article-title":"Automatic multiscale vascular image segmentation algorithm for coronary angiography","volume":"46","author":"Carballal","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cervantes-Sanchez, F., Cruz-Aceves, I., Hernandez-Aguirre, A., Hernandez-Gonzalez, M.A., and Solorio-Meza, S.E. (2019). Automatic segmentation of coronary arteries in X-ray angiograms using multiscale analysis and artificial neural networks. Appl. Sci., 9.","DOI":"10.3390\/app9245507"},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_38","unstructured":"Loshchilov, I., and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sule, O., and Viriri, S. (2020). Enhanced convolutional neural networks for segmentation of retinal blood vessel image. Proceedings of the 2020 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 11\u201312 March 2020, IEEE.","DOI":"10.1109\/ICTAS47918.2020.233996"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8365783","DOI":"10.1155\/2020\/8365783","article-title":"A hybrid unsupervised approach for retinal vessel segmentation","volume":"2020","author":"Khan","year":"2023","journal-title":"BioMed Res. Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104551","DOI":"10.1016\/j.compbiomed.2021.104551","article-title":"IBA-U-Net: Attentive BConvLSTM U-Net with redesigned inception for medical image seg- mentation","volume":"135","author":"Chen","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106198","DOI":"10.1016\/j.compbiomed.2022.106198","article-title":"Do you need sharpened details? Asking MMDC- Net: Multi-layer multi-scale dilated convolution network for retinal vessel segmentation","volume":"150","author":"Zhong","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, J., Gao, G., Yang, L., Liu, Y., and Yu, H. (2022). DEF-Net: A dual-encoder fusion network for fundus retinal vessel segmentation. Electronics, 11.","DOI":"10.3390\/electronics11223810"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105651","DOI":"10.1016\/j.compbiomed.2022.105651","article-title":"CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation","volume":"147","author":"Dong","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106341","DOI":"10.1016\/j.compbiomed.2022.106341","article-title":"Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images","volume":"152","author":"Liu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"119443","DOI":"10.1016\/j.eswa.2022.119443","article-title":"Orientation and context entangled network for retinal vessel segmentation","volume":"217","author":"Wei","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"113553","DOI":"10.1016\/j.measurement.2023.113553","article-title":"A multi-scale global attention network for blood vessel segmentation from fundus images","volume":"222","author":"Gao","year":"2023","journal-title":"Measurement"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ijmedinf.2019.03.015","article-title":"BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation","volume":"126","author":"Guo","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhang, X., Huang, S., Wang, Q., and Chen, F. (2020). CTF-Net: Retinal vessel segmentation via deep coarse-to-fine supervision network. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3\u20137 April 2020, IEEE.","DOI":"10.1109\/ISBI45749.2020.9098742"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"131257","DOI":"10.1109\/ACCESS.2020.3008899","article-title":"Residual connection-based encoder decoder network (RCED-Net) for retinal vessel segmentation","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.1109\/JBHI.2020.3011178","article-title":"CSU-Net: A context spatial U-Net for accurate blood vessel segmentation in fundus images","volume":"25","author":"Wang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2021.03.085","article-title":"A hybrid deep segmentation network for fundus vessels via deep-learning framework","volume":"448","author":"Yang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_53","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 16\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mou, L., Zhao, Y., Chen, L., Cheng, J., Gu, Z., Hao, H., Qi, H., Zheng, Y., Frangi, A., and Liu, J. (2019). CS-Net: Channel and spatial attention network for curvilinear structure segmentation. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019: 22nd International Conference, Shenzhen, China, 13\u201317 October 2019, Springer.","DOI":"10.1007\/978-3-030-32239-7_80"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"742","DOI":"10.54287\/gujsa.1575986","article-title":"UKnow-Net: Knowledge-Enhanced U-Net for Improved Retinal Vessel Segmentation","volume":"11","year":"2024","journal-title":"Gazi Univ. J. Sci. Part A Eng. Innov."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, J., Liu, X., Zheng, S., Zhang, W., and Gu, J. (2024). Dual-field microvascular segmentation: Hemodynamically-consistent attention learning for retinal vasculature mapping. bioRxiv.","DOI":"10.1101\/2024.11.27.625635"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5573","DOI":"10.1109\/JBHI.2024.3411658","article-title":"CFI-Net: A choquet fuzzy integral based ensemble network with PSO-optimized fuzzy measures for diagnosing multiple skin diseases including Mpox","volume":"28","author":"Asif","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1109\/TMI.2022.3151666","article-title":"Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation","volume":"41","author":"Li","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"38263","DOI":"10.1109\/ACCESS.2023.3265729","article-title":"Block attention and switchable normalization based deep learning framework for segmentation of retinal vessels","volume":"11","author":"Deari","year":"2023","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/TETCI.2023.3306250","article-title":"Multi-level medical image segmentation network based on multi-scale and context information fusion strategy","volume":"8","author":"Tan","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T10:20:37Z","timestamp":1779272437000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":61,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jimaging12050216"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12050216","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,20]]}}}