{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T11:10:10Z","timestamp":1778325010372,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00521-026-12024-z","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T04:57:10Z","timestamp":1775883430000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GBAT-UNet: group bottleneck transformer with attention in UNet for retinal vessel segmentation"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3510-8815","authenticated-orcid":false,"given":"Ananya","family":"Bose","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1804-7360","authenticated-orcid":false,"given":"Prerana","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7365-3924","authenticated-orcid":false,"given":"Anasua","family":"Sarkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"12024_CR1","doi-asserted-by":"crossref","unstructured":"Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"12024_CR2","unstructured":"Andrews L, Sinclair S (1988) Macro-vascular topography in diabetic retinopathy: Analysis of fundus pictures using high resolution digital image processing. In: ARVO Abstract, 262"},{"key":"12024_CR3","doi-asserted-by":"crossref","unstructured":"Aqeel AF, Ganesan S (2011) Retinal image segmentation using texture, thresholding, and morphological operations. In: 2011 IEEE Int Conf electro\/Inf Technol pp 1\u20136. IEEE","DOI":"10.1109\/EIT.2011.5978564"},{"issue":"9","key":"12024_CR4","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1007\/s00521-023-09304-3","volume":"36","author":"R Bala","year":"2024","unstructured":"Bala R, Sharma A, Goel N (2024) Ctnet: convolutional transformer network for diabetic retinopathy classification. Neural Comput Appl 36(9):4787\u20134809","journal-title":"Neural Comput Appl"},{"key":"12024_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2025.103185","volume":"14","author":"M Bali","year":"2025","unstructured":"Bali M, Mishra VP, Yenkikar A, Chikmurge D (2025) Quantumnet: an enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning. MethodsX 14:103185","journal-title":"MethodsX"},{"issue":"3","key":"12024_CR6","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/42.34715","volume":"8","author":"S Chaudhuri","year":"1989","unstructured":"Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263\u2013269","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"12024_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0262689","volume":"17","author":"D Chen","year":"2022","unstructured":"Chen D, Yang W, Wang L, Tan S, Lin J, Bu W (2022) Pcat-unet: unet-like network fused convolution and transformer for retinal vessel segmentation. PLoS One 17(1):e0262689","journal-title":"PLoS One"},{"key":"12024_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105651","volume":"147","author":"F Dong","year":"2022","unstructured":"Dong F, Wu D, Guo C, Zhang S, Yang B, Gong X (2022) Craunet: a cascaded residual attention u-net for retinal vessel segmentation. Comput Biol Med 147:105651","journal-title":"Comput Biol Med"},{"issue":"3","key":"12024_CR9","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1002\/jemt.23596","volume":"84","author":"NA El-Hag","year":"2021","unstructured":"El-Hag NA, Sedik A, El-Shafai W, El-Hoseny HM, Khalaf AA, El-Fishawy AS, Al-Nuaimy W, Abd El-Samie FE, El-Banby GM (2021) Classification of retinal images based on convolutional neural network. Microsc Res Tech 84(3):394\u2013414","journal-title":"Microsc Res Tech"},{"key":"12024_CR10","doi-asserted-by":"crossref","unstructured":"Fu H, Xu Y, Lin S, Kee Wong DW, Liu J (2016) Deepvessel: Retinal vessel segmentation via deep learning and conditional random field. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th Int Conf, Athens, Greece, October 17\u201321, 2016, Proc Part II 19, pp 132\u2013139. Springer","DOI":"10.1007\/978-3-319-46723-8_16"},{"key":"12024_CR11","doi-asserted-by":"crossref","unstructured":"Fu H, Xu Y, Wong DWK, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th Int Sympos Bio Imag (ISBI), pp 698\u2013701. IEEE","DOI":"10.1109\/ISBI.2016.7493362"},{"key":"12024_CR12","doi-asserted-by":"crossref","unstructured":"Gao X, Cai Y, Qiu C, Cui Y (2017) Retinal blood vessel segmentation based on the gaussian matched filter and u-net. In: 2017 10th Int Cong Image and Signal Proc, BioMed Eng Inf (CISP-BMEI), pp 1\u20135. IEEE","DOI":"10.1109\/CISP-BMEI.2017.8302199"},{"key":"12024_CR13","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.patrec.2025.01.019","volume":"189","author":"MN Getahun","year":"2025","unstructured":"Getahun MN, Rogov OY, Dylov DV, Somov A, Bouridane A, Hamoudi R (2025) Fs-net: full scale network and adaptive threshold for improving extraction of micro-retinal vessel structures. Pattern Recognit Lett 189:188\u2013194","journal-title":"Pattern Recognit Lett"},{"key":"12024_CR14","doi-asserted-by":"crossref","unstructured":"Guo C, Szemenyei M, Hu Y, Wang W, Zhou W, Yi Y (2021) Channel attention residual u-net for retinal vessel segmentation. In: ICASSP 2021\u20132021 IEEE Int Conf Acous, Speech and Signal Proc (ICASSP), pp 1185\u20131189. IEEE","DOI":"10.1109\/ICASSP39728.2021.9414282"},{"key":"12024_CR15","doi-asserted-by":"crossref","unstructured":"Guo C, Szemenyei M, Yi Y, Wang W, Chen B, Fan C (2021) Sa-unet: Spatial attention u-net for retinal vessel segmentation. In: 2020 25th Int Conf Pattern Recog (ICPR), pp 1236\u20131242. IEEE","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"12024_CR16","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2021) Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: Int MICCAI Brainlesion Workshop, pp 272\u2013284. Springer","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"12024_CR17","unstructured":"Jain A, Gupta R, Singhal J (2024) Diabetic retinopathy detection using quantum transfer learning. arXiv preprint arXiv:2405.01734"},{"key":"12024_CR18","unstructured":"Javed S, Khan TM, Qayyum A, Alinejad-Rokny H, Sowmya A, Razzak I (2024) Advancing medical image segmentation with mini-net: A lightweight solution tailored for efficient segmentation of medical images. arXiv preprint arXiv:2405.17520"},{"issue":"12","key":"12024_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/s22124592","volume":"22","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Liang J, Cheng T, Lin X, Zhang Y, Dong J (2022) Mtpa_unet: multi-scale transformer-position attention retinal vessel segmentation network joint transformer and cnn. Sensors 22(12):4592","journal-title":"Sensors"},{"key":"12024_CR20","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","volume":"178","author":"Q Jin","year":"2019","unstructured":"Jin Q, Meng Z, Pham TD, Chen Q, Wei L, Su R (2019) Dunet: a deformable network for retinal vessel segmentation. Knowl Based Syst 178:149\u2013162","journal-title":"Knowl Based Syst"},{"issue":"10s","key":"12024_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM Computing Surveys (CSUR) 54(10s):1\u201341","journal-title":"ACM computing surveys (CSUR)"},{"key":"12024_CR22","doi-asserted-by":"publisher","first-page":"70853","DOI":"10.1109\/ACCESS.2023.3294443","volume":"11","author":"BN Kumar","year":"2023","unstructured":"Kumar BN, Mahesh T, Geetha G, Guluwadi S (2023) Redefining retinal lesion segmentation: a quantum leap with dl-unet enhanced auto encoder-decoder for fundus image analysis. IEEE Access 11:70853\u201370864","journal-title":"IEEE Access"},{"issue":"17","key":"12024_CR23","doi-asserted-by":"publisher","first-page":"12495","DOI":"10.1007\/s00521-023-08402-6","volume":"35","author":"KS Kumar","year":"2023","unstructured":"Kumar KS, Singh NP (2023) Retinal disease prediction through blood vessel segmentation and classification using ensemble-based deep learning approaches. Neural Comput Appl 35(17):12495\u201312511","journal-title":"Neural Comput Appl"},{"key":"12024_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09851-3","author":"P Kumari","year":"2024","unstructured":"Kumari P, Saxena P (2024) Pathologic myopia diagnosis and localization from retinal fundus images using custom CNN. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-024-09851-3","journal-title":"Neural Comput Appl"},{"key":"12024_CR25","doi-asserted-by":"crossref","unstructured":"Li L, Verma M, Nakashima Y, Nagahara H, Kawasaki R (2020) Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. In: Proc IEEE\/CVF winter Conf Appl Comput Vision pp 3656\u20133665","DOI":"10.1109\/WACV45572.2020.9093621"},{"issue":"1","key":"12024_CR26","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","volume":"35","author":"Q Li","year":"2015","unstructured":"Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2015) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109\u2013118","journal-title":"IEEE Trans Med Imaging"},{"key":"12024_CR27","doi-asserted-by":"crossref","unstructured":"Li Y, Wang S, Wang J, Zeng G, Liu W, Zhang Q, Jin Q, Wang Y (2021) Gt u-net: A u-net like group transformer network for tooth root segmentation. In: Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proc 12:386\u2013395. Springer","DOI":"10.1007\/978-3-030-87589-3_40"},{"key":"12024_CR28","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.jvcir.2018.10.001","volume":"56","author":"S Lian","year":"2018","unstructured":"Lian S, Luo Z, Zhong Z, Lin X, Su S, Li S (2018) Attention guided u-net for accurate iris segmentation. J Vis Commun Image Represent 56:296\u2013304","journal-title":"J Vis Commun Image Represent"},{"issue":"11","key":"12024_CR29","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2016","unstructured":"Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369\u20132380","journal-title":"IEEE Trans Med Imaging"},{"key":"12024_CR30","doi-asserted-by":"crossref","unstructured":"Liu J, Yang H, Zhou HY, Xi Y, Yu L, Li C, Liang Y, Shi G, Yu Y, Zhang S et al (2024) Swin-umamba: Mamba-based unet with imagenet-based pretraining. In: Int Conf Med Image Comput Comput-Assisted Int pp 615\u2013625. Springer","DOI":"10.1007\/978-3-031-72114-4_59"},{"key":"12024_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3188710","author":"W Liu","year":"2022","unstructured":"Liu W, Yang H, Tian T, Cao Z, Pan X, Xu W, Jin Y, Gao F (2022) Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2022.3188710","journal-title":"IEEE J Biomed Health Inform"},{"key":"12024_CR32","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc IEEE\/CVF Int Conf Comput vision pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"1","key":"12024_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-77464-w","volume":"14","author":"N Lv","year":"2024","unstructured":"Lv N, Xu L, Chen Y, Sun W, Tian J, Zhang S (2024) Tcddu-net: combining transformer and convolutional dual-path decoding u-net for retinal vessel segmentation. Sci Rep 14(1):25978","journal-title":"Sci Rep"},{"key":"12024_CR34","doi-asserted-by":"crossref","unstructured":"Mehmood M, Alsharari M, Iqbal S, Spence I, Fahim M (2024) Retinalitenet: A lightweight transformer based cnn for retinal feature segmentation. In: Proc IEEE\/CVF Conf Comput vision and Pattern Recog pp 2454\u20132463","DOI":"10.1109\/CVPRW63382.2024.00252"},{"key":"12024_CR35","doi-asserted-by":"crossref","unstructured":"Mostafiz T, Jarin I, Fattah SA, Shahnaz C (2018) Retinal blood vessel segmentation using residual block incorporated u-net architecture and fuzzy inference system. In: 2018 IEEE Int WIE Conf Elect Comput Eng (WIECON-ECE), 106\u2013109. IEEE","DOI":"10.1109\/WIECON-ECE.2018.8783182"},{"key":"12024_CR36","doi-asserted-by":"crossref","unstructured":"Mou L, Zhao Y, Chen L, Cheng J, Gu Z, Hao H, Qi H, Zheng Y, Frangi A, Liu J (2019) Cs-net: Channel and spatial attention network for curvilinear structure segmentation. In: Medical Image Computing and Computer Assisted Intervention-MICCAI 2019: 22nd Int Conf, Shenzhen, China, October 13\u201317, Proc, Part I 22, 721\u2013730. Springer (2019)","DOI":"10.1007\/978-3-030-32239-7_80"},{"issue":"29","key":"12024_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17485\/ijst\/2015\/v8i29\/73142","volume":"8","author":"K Narasimhan","year":"2015","unstructured":"Narasimhan K, Vijayarekha K (2015) Automatic grading of images based on retinal vessel tortuosity analysis. Indian J Sci Technol 8(29):1","journal-title":"Indian J Sci Technol"},{"key":"12024_CR38","doi-asserted-by":"crossref","unstructured":"Niemeijer M, Staal J, Van Ginneken B, Loog M, Abramoff MD (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical Imaging 2004: Image Proc 5370:648\u2013656. SPIE","DOI":"10.1117\/12.535349"},{"key":"12024_CR39","doi-asserted-by":"crossref","unstructured":"Ouyang Y, Kuang X, Xiong M, Wang Z, Wang Y (2025) A novel hybrid approach for retinal vessel segmentation with dynamic long-range dependency and multi-scale retinal edge fusion enhancement. arXiv preprint arXiv:2504.13553","DOI":"10.1007\/s10044-025-01534-6"},{"key":"12024_CR40","doi-asserted-by":"crossref","unstructured":"Pham TD, Tran DT, Brown M, Kennedy RL (2005) Image segmentation of retinal vessels by fuzzy models. In: 2005 International Symposium on Intelligent Signal Processing and Communication Systems, pp 541\u2013544. IEEE","DOI":"10.1109\/ISPACS.2005.1595466"},{"issue":"4","key":"12024_CR41","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/JBHI.2023.3237704","volume":"27","author":"Z Qu","year":"2023","unstructured":"Qu Z, Zhuo L, Cao J, Li X, Yin H, Wang Z (2023) Tp-net: two-path network for retinal vessel segmentation. IEEE J Biomed Health Inform 27(4):1979\u20131990","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"12024_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-48039-y","volume":"13","author":"K Radha","year":"2023","unstructured":"Radha K, Yepuganti K, Saritha S, Kamireddy C, Bavirisetti DP (2023) Unfolded deep kernel estimation-attention unet-based retinal image segmentation. Sci Rep 13(1):20712","journal-title":"Sci Rep"},{"key":"12024_CR43","first-page":"12116","volume":"34","author":"M Raghu","year":"2021","unstructured":"Raghu M, Unterthiner T, Kornblith S, Zhang C, Dosovitskiy A (2021) Do vision transformers see like convolutional neural networks? Adv Neural Inf Process Syst 34:12116\u201312128","journal-title":"Adv Neural Inf Process Syst"},{"key":"12024_CR44","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS, 9351, 234\u2013241. Springer . http:\/\/lmb.informatik.uni-freiburg.de\/Publications\/2015\/RFB15a. (available on arXiv:1505.04597 [cs.CV])","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"12024_CR45","unstructured":"Sadeghzadeh R, Berks M, Astley S, Taylor C (2010) Detection of retinal blood vessels using complex wavelet transforms and random forest classification. Proceedings of Medical Image Understanding and Analysis (MIUA) 127\u2013131"},{"key":"12024_CR46","doi-asserted-by":"crossref","unstructured":"Si Z, Fu D, Li J (2019) U-net with attention mechanism for retinal vessel segmentation. In: Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23\u201325, 2019, Proceedings, Part II 10, 668\u2013677. Springer","DOI":"10.1007\/978-3-030-34110-7_56"},{"issue":"1","key":"12024_CR47","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0191827","volume":"13","author":"S Soomro","year":"2018","unstructured":"Soomro S, Munir A, Choi KN (2018) Hybrid two-stage active contour method with region and edge information for intensity inhomogeneous image segmentation. PLoS One 13(1):e0191827","journal-title":"PLoS One"},{"key":"12024_CR48","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1109\/ACCESS.2018.2794463","volume":"6","author":"TA Soomro","year":"2018","unstructured":"Soomro TA, Khan TM, Khan MA, Gao J, Paul M, Zheng L (2018) Impact of ICA-based image enhancement technique on retinal blood vessels segmentation. IEEE Access 6:3524\u20133538","journal-title":"IEEE Access"},{"key":"12024_CR49","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.bspc.2018.04.016","volume":"44","author":"CL Srinidhi","year":"2018","unstructured":"Srinidhi CL, Aparna P, Rajan J (2018) A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images. Biomed Signal Process Control 44:110\u2013126","journal-title":"Biomed Signal Process Control"},{"key":"12024_CR50","doi-asserted-by":"publisher","unstructured":"Talaat FM, Ali AAA, ElGendy R, ELShafie MA (2024) Deep attention for enhanced oct image analysis in clinical retinal diagnosis. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-024-10450-5","DOI":"10.1007\/s00521-024-10450-5"},{"issue":"3","key":"12024_CR51","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0031-3203(88)90057-X","volume":"21","author":"S Tamura","year":"1988","unstructured":"Tamura S, Okamoto Y, Yanashima K (1988) Zero-crossing interval correction in tracing eye-fundus blood vessels. Pattern Recogn 21(3):227\u2013233","journal-title":"Pattern Recogn"},{"key":"12024_CR52","first-page":"116","volume":"80","author":"M Tanaka","year":"1980","unstructured":"Tanaka M, Tanaka K (1980) An automatic technique for fundus-photograph mosaic and vascular net reconstruction. Medinfo 80:116\u2013120","journal-title":"Medinfo"},{"key":"12024_CR53","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM (2021) Medical transformer: Gated axial-attention for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th Int Conf, Stras, France, September 27-October 1, 2021, Proc, Part I 24, pp 36\u201346. Springer","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"12024_CR54","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.neucom.2014.07.059","volume":"149","author":"S Wang","year":"2015","unstructured":"Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708\u2013717","journal-title":"Neurocomputing"},{"key":"12024_CR55","doi-asserted-by":"crossref","unstructured":"Wu C, Zou Y, Yang Z (2019) U-gan: Generative adversarial networks with u-net for retinal vessel segmentation. In: 2019 14th Int Conf Comput Sci & Educat (ICCSE), 642\u2013646. IEEE","DOI":"10.1109\/ICCSE.2019.8845397"},{"key":"12024_CR56","unstructured":"Xiancheng W, Wei L, Bingyi M, He J, Jiang Z, Xu W, Ji Z, Hong G, Zhaomeng S (2018) Retina blood vessel segmentation using a u-net based convolutional neural network. In: Procedia Comput Sci: Int Conf Data Sci (ICDS 2018) 8\u20139"},{"issue":"1","key":"12024_CR57","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/JBHI.2021.3089201","volume":"26","author":"Y Yuan","year":"2021","unstructured":"Yuan Y, Zhang L, Wang L, Huang H (2021) Multi-level attention network for retinal vessel segmentation. IEEE J Biomed Health Inform 26(1):312\u2013323","journal-title":"IEEE J Biomed Health Inform"},{"key":"12024_CR58","doi-asserted-by":"crossref","unstructured":"Zhang H, Zhong X, Li Z, Chen Y, Zhu Z, Lv J, Li C, Zhou Y, Li G et al (2022) Tim-net: transformer in m-net for retinal vessel segmentation. J Heal Eng 2022","DOI":"10.1155\/2022\/9016401"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12024-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-12024-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12024-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:57:09Z","timestamp":1778324229000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-12024-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":58,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["12024"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-12024-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"29 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"274"}}