{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:27:56Z","timestamp":1772828876035,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62361027"],"award-info":[{"award-number":["62361027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62161011"],"award-info":[{"award-number":["62161011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Research and Development Plan of Jiangxi Provincial Science and Technology Department","award":["20223BBE51036"],"award-info":[{"award-number":["20223BBE51036"]}]},{"name":"the Humanity and Social Science Fund of Ministry of Education of China","award":["23YJA870005"],"award-info":[{"award-number":["23YJA870005"]}]},{"name":"the Natural Science Foundation of Jiangxi Provincial Department of Science and Technology","award":["20232BAB202004"],"award-info":[{"award-number":["20232BAB202004"]}]},{"name":"the Natural Science Foundation of Jiangxi Provincial Department of Science and Technology","award":["20232BAB202022"],"award-info":[{"award-number":["20232BAB202022"]}]},{"name":"the Humanity and Social Science Foundation of the Jiangxi Province","award":["22TQ01"],"award-info":[{"award-number":["22TQ01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01714-7","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T21:11:40Z","timestamp":1735852300000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MSM-TDE: multi-scale semantics mining and tiny details enhancement network for retinal vessel segmentation"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8375-0039","authenticated-orcid":false,"given":"Hongbin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Ya","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Guangli","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jingqin","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"key":"1714_CR1","doi-asserted-by":"publisher","unstructured":"Bilal H, Yao W, Guo Y et al (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties [C]. In: The 36th Chinese control conference (CCC 2017). https:\/\/doi.org\/10.23919\/ChiCC.2017.8028015","DOI":"10.23919\/ChiCC.2017.8028015"},{"key":"1714_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08026-x","author":"B Hazrat","year":"2023","unstructured":"Hazrat B, Baoqun Y, Shamrooz AM et al (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator [J]. Soft Comput Fusion Found Methodol Appl. https:\/\/doi.org\/10.1007\/s00500-023-08026-x","journal-title":"Soft Comput Fusion Found Methodol Appl"},{"key":"1714_CR3","doi-asserted-by":"publisher","first-page":"4029","DOI":"10.1007\/s00500-023-07923-5","volume":"27","author":"H Bilal","year":"2023","unstructured":"Bilal H, Yin B, Kumar A, Ali MH, Zhang J, Yao J (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27:4029\u20134039. https:\/\/doi.org\/10.1007\/s00500-023-07923-5","journal-title":"Soft Comput"},{"issue":"21","key":"1714_CR4","doi-asserted-by":"publisher","first-page":"16373","DOI":"10.1007\/s00500-023-09164-y","volume":"27","author":"D Huili","year":"2023","unstructured":"Huili D, Yuanyuan L, Hazrat HB (2023) A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways [J]. Soft Comput Fusion Found Methodol Appl 27(21):16373\u201316388. https:\/\/doi.org\/10.1007\/s00500-023-09164-y","journal-title":"Soft Comput Fusion Found Methodol Appl"},{"issue":"23","key":"1714_CR5","doi-asserted-by":"publisher","first-page":"18195","DOI":"10.1007\/s00500-023-09278-3","volume":"27","author":"Q Wu","year":"2023","unstructured":"Wu Q, Li X, Wang KBH (2023) Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles [J]. Soft Comput Fusion Found Methodol Appl 27(23):18195\u201318213. https:\/\/doi.org\/10.1007\/s00500-023-09278-3","journal-title":"Soft Comput Fusion Found Methodol Appl"},{"issue":"1","key":"1714_CR6","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.cmpb.2012.03.009","volume":"108","author":"M Fraz","year":"2012","unstructured":"Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A (2012) Blood vessel segmentation methodologies in retinal images a survey. Methods Programs Biomed 108(1):407\u2013433","journal-title":"Methods Programs Biomed"},{"key":"1714_CR7","doi-asserted-by":"crossref","unstructured":"Rahim S et al (2019) Detection of diabetic retinopathy and maculopathy in eye fundus images using deep learning and image augmentation. In: International cross domain conference for machine learning and knowledge extraction. Springer, pp 114\u2013127","DOI":"10.1007\/978-3-030-29726-8_8"},{"key":"1714_CR8","doi-asserted-by":"publisher","first-page":"782","DOI":"10.3390\/app11020782","volume":"11","author":"A Comelli","year":"2021","unstructured":"Comelli A, Dahiya N, Stefano A, Vernuccio F, Portoghese M, Cutaia G, Bruno A, Salvaggio G, Yezzi A (2021) Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Appl Sci 11:782","journal-title":"Appl Sci"},{"key":"1714_CR9","doi-asserted-by":"publisher","first-page":"105618","DOI":"10.1016\/j.compbiomed.2022.105618","volume":"146","author":"H Su","year":"2022","unstructured":"Su H, Zhao D, Elmannai H, Heidari A, Bourouis S, Wu Z, Cai Z, Gui W, Chen M (2022) Multilevel threshold image segmentation for COVID-19 chest radiography: a framework using horizontal and vertical multiverse optimization. Comput Biol Med 146:105618","journal-title":"Comput Biol Med"},{"key":"1714_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105810","author":"A Qi","year":"2022","unstructured":"Qi A, Zhao D, Yu F, Heidari A, Wu Z, Cai Z, Alenezi F, Mansour R, Chen H, Chen M (2022) Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Comput Biol Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105810","journal-title":"Comput Biol Med"},{"issue":"1","key":"1714_CR11","first-page":"211","volume":"16","author":"W Wang","year":"2022","unstructured":"Wang W, Tu A, Bergholm F (2022) Improved minimum spanning tree based image segmentation with guided matting. KSII Trans Internet Inf Syst (TIIS) 16(1):211\u2013230","journal-title":"KSII Trans Internet Inf Syst (TIIS)"},{"issue":"3","key":"1714_CR12","doi-asserted-by":"publisher","first-page":"e12822","DOI":"10.1111\/exsy.12822","volume":"39","author":"B He","year":"2022","unstructured":"He B, Hu W, Zhang K, Yuan S, Han X, Su C, Zhao J, Wang G, Wang G, Zhang L (2022) Image segmentation algorithm of lung cancer based on neural network model. Expet Syst 39(3):e12822","journal-title":"Expet Syst"},{"issue":"12","key":"1714_CR13","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1109\/TMI.2016.2587062","volume":"35","author":"J Zhang","year":"2016","unstructured":"Zhang J, Dashtbozorg B, Bekkers E, Pluim JP, Duits R, Ter Haar Romeny BM (2016) Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans Med Imag 35(12):2631","journal-title":"IEEE Trans Med Imag"},{"issue":"3","key":"1714_CR14","doi-asserted-by":"publisher","first-page":"399","DOI":"10.2478\/v10006-008-0036-5","volume":"18","author":"Y Yang","year":"2008","unstructured":"Yang Y, Huang S, Rao N (2008) An automatic hybrid method for retinal blood vessel extraction. Int J Appl Math Comput Sci 18(3):399","journal-title":"Int J Appl Math Comput Sci"},{"issue":"18","key":"1714_CR15","doi-asserted-by":"publisher","first-page":"25221","DOI":"10.1007\/s11042-019-7719-9","volume":"78","author":"R Ghoshal","year":"2019","unstructured":"Ghoshal R, Saha A, Das S (2019) An improved vessel extraction scheme from retinal fundus images. Multimed Tool Appl 78(18):25221\u201325239","journal-title":"Multimed Tool Appl"},{"key":"1714_CR16","doi-asserted-by":"crossref","unstructured":"Salazar-Gonzalez AG, Li Y, Liu X (2010) Retinal blood vessel segmentation via graph cut. In: 2010 11th Inter-national conference on control automation robotics & vision, IEEE, pp 225\u2013230","DOI":"10.1109\/ICARCV.2010.5707265"},{"issue":"1","key":"1714_CR17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.media.2006.11.004","volume":"11","author":"ME Martinez-Perez","year":"2007","unstructured":"Martinez-Perez ME, Hughes AD, Thom SA, Bharath AA, Parker KH (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 11(1):47\u201361","journal-title":"Med Image Anal"},{"issue":"9","key":"1714_CR18","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1109\/TMI.2006.879967","volume":"25","author":"JV Soares","year":"2006","unstructured":"Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imag 25(9):1214\u20131222","journal-title":"IEEE Trans Med Imag"},{"key":"1714_CR19","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer assisted intervention, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1714_CR20","doi-asserted-by":"publisher","first-page":"6753","DOI":"10.1007\/s40747-023-01095-3","volume":"9","author":"J Ouyang","year":"2023","unstructured":"Ouyang J, Liu S, Peng H, Garg H, Thanh DNH (2023) LEA U-Net: a U-Net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation. Complex Intell Syst 9:6753\u20136766","journal-title":"Complex Intell Syst"},{"key":"1714_CR21","first-page":"106198","volume":"150","author":"X Zhong","year":"2022","unstructured":"Zhong X, Zhang H, Li G, Ji DJ (2022) Do you need sharpened details? Asking MMDC-Net: multi-layer multiscale dilated convolution network for retinal vessel segmentation. Medicine 150:106198","journal-title":"Medicine"},{"key":"1714_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106341","volume":"152","author":"Y Liu","year":"2023","unstructured":"Liu Y, Shen J, Yang L, Yu H, Bian G (2023) Wave-net: a lightweight deep network for retinal vessel segmentation from fundus images. Comput Biol Med 152:106341","journal-title":"Comput Biol Med"},{"key":"1714_CR23","doi-asserted-by":"publisher","first-page":"113553","DOI":"10.1016\/j.measurement.2023.113553","volume":"222","author":"G Gao","year":"2023","unstructured":"Gao G, Li J, Yang L, Liu Y (2023) A multi-scale global attention network for blood vessel segmentation from fundus images. Measurement 222:113553","journal-title":"Measurement"},{"key":"1714_CR24","volume-title":"UTNet: a hybrid transformer architecture for medical image segmentation","author":"Y Gao","year":"2021","unstructured":"Gao Y, Zhou M, Metaxas DN (2021) UTNet: a hybrid transformer architecture for medical image segmentation. Springer, Berlin"},{"key":"1714_CR25","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Houlsby N (2021) An image is worth 16\u00a0\u00d7\u00a016 words: transformers for image recognition at scale. In: ICLR. Corpus ID: 225039882"},{"key":"1714_CR26","doi-asserted-by":"publisher","first-page":"103472","DOI":"10.1016\/j.bspc.2021.103472","volume":"73","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Fang J, Chen Y et al (2022) Edge-aware U-net with gated convolution for retinal vessel segmentation [J]. Biomed Signal Process Control 73:103472","journal-title":"Biomed Signal Process Control"},{"key":"1714_CR27","doi-asserted-by":"crossref","unstructured":"Wang H, Cao P, Wang J et al (2021) UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer [J]","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"1714_CR28","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: a nested U- net architecture for medical image segmentation. In: Deep learning in medical image analysis and multi-modal learning for clinical decision support. Springer, Berlin, pp 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"1714_CR29","doi-asserted-by":"publisher","unstructured":"Huang H, Lin L, Tong R et al (2020) UNet 3+: a full-scale connected UNet for medical image segmentation [J]. arXiv. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"1714_CR30","doi-asserted-by":"publisher","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. https:\/\/doi.org\/10.48550\/arXiv.2102.04306","DOI":"10.48550\/arXiv.2102.04306"},{"issue":"10","key":"1714_CR31","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H et al (2019) Ce-net: context encoder network for 2d medical image segmentation [J]. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"key":"1714_CR32","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1155\/2021\/5520407","volume":"2021","author":"ZH Huang","year":"2021","unstructured":"Huang ZH, Fang Y, Huang H, Xu X, Wang J, Lai X (2021) Automatic retinal vessel segmentation based on an improved U-Net approach. Sci Program 2021:15. https:\/\/doi.org\/10.1155\/2021\/5520407","journal-title":"Sci Program"},{"key":"1714_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101874","volume":"67","author":"L Mou","year":"2021","unstructured":"Mou L, Zhao Y, Fu H, Liu Y, Cheng J, Zheng Y, Su P, Yang J, Chen L, Frangi AF et al (2021) CS2-Net: deep learning segmentation of curvilinear structures in medical imaging. Med Image Anal 67:101874. https:\/\/doi.org\/10.1016\/j.media.2020.101874","journal-title":"Med Image Anal"},{"key":"1714_CR34","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2020","unstructured":"Lin T, Goyal P, Girshick RB, He K, Dollar P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42:318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1714_CR35","doi-asserted-by":"publisher","first-page":"116256","DOI":"10.1016\/j.eswa.2022.116526","volume":"195","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, He M, Chen Z et al (2022) Bridge-Net: context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation [J]. Expert Syst Appl 195:116256","journal-title":"Expert Syst Appl"},{"key":"1714_CR36","doi-asserted-by":"publisher","first-page":"6762","DOI":"10.1038\/s41598-023-33357-y","volume":"13","author":"S Jin","year":"2023","unstructured":"Jin S, Yu S, Peng J, Wang H, Zhao YJSR (2023) A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning. Sci Reports 13:6762. https:\/\/doi.org\/10.1038\/s41598-023-33357-y","journal-title":"Sci Reports"},{"key":"1714_CR37","doi-asserted-by":"publisher","unstructured":"Chen W, Zhang Y, He J, Qiao Y, Chen Y, Shi H, Tang X (2019) Prostate segmentation using 2D bridged U-net. In: 2019 International joint conference on neural networks, IJCNN, pp 1\u20137. https:\/\/doi.org\/10.1109\/IJCNN.2019.8851908","DOI":"10.1109\/IJCNN.2019.8851908"},{"key":"1714_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107269","volume":"164","author":"W Zhou","year":"2023","unstructured":"Zhou W, Bai W, Ji J, Yi Y, Zhang N, Cui W (2023) Dual-path multi-scale context dense aggregation network for retinal vessel segmentation. Comput Biol Med 164:107269","journal-title":"Comput Biol Med"},{"key":"1714_CR39","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/978-3-030-32239-7_88","volume-title":"Attention guided network for retinal image segmentation","author":"S Zhang","year":"2019","unstructured":"Zhang S, Fu H, Yan Y, Zhang Y, Wu Q, Yang M, Tan M, Xu Y (2019) Attention guided network for retinal image segmentation. Springer, Cham, pp 797\u2013805. https:\/\/doi.org\/10.1007\/978-3-030-32239-7_88"},{"key":"1714_CR40","doi-asserted-by":"publisher","unstructured":"Li L, Verma M, Nakashima Y, Nagahara H, Kawasaki R (2020) IterNet: Retinal image segmentation utilizing structural redundancy in vessel networks. In: Proceedings of the IEEE\/CVFWinter conference on applications of computer vision, pp 3656\u20133665. https:\/\/doi.org\/10.1109\/WACV45572.2020.9093621","DOI":"10.1109\/WACV45572.2020.9093621"},{"key":"1714_CR41","doi-asserted-by":"publisher","first-page":"4551","DOI":"10.1109\/JBHI.2022.3182471","volume":"26","author":"Y Ye","year":"2022","unstructured":"Ye Y, Pan C, Yicheng Wu, Wang S, Xia Y (2022) MFI-Net: multiscale feature interaction network for retinal vessel segmentation. IEEE J Biomed Health Inform 26:4551\u20134562","journal-title":"IEEE J Biomed Health Inform"},{"key":"1714_CR42","doi-asserted-by":"publisher","unstructured":"Guo C, Szemenyei M, Yi Y, Wang W, Chen B, Fan CJ (2020) SA-UNet: spatial attention U-Net for retinal vessel segmentation. arXiv:2004.03696. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413346","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"1714_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104426","volume":"81","author":"J Li","year":"2023","unstructured":"Li J, Gao G, Yang L, Liu Y (2023) GDF-Net: a multi-task symmetrical network for retinal vessel segmentation. Biomed Signal Process Control 81:104426. https:\/\/doi.org\/10.1016\/j.bspc.2022.104426","journal-title":"Biomed Signal Process Control"},{"key":"1714_CR44","doi-asserted-by":"publisher","unstructured":"Oktay O, Schlemper J, Le Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert DJ (2018) Attention U-Net: learning where to look for the pancreas. arXiv:1804.03999. https:\/\/doi.org\/10.48550\/arXiv.1804.03999","DOI":"10.48550\/arXiv.1804.03999"},{"key":"1714_CR45","doi-asserted-by":"publisher","first-page":"106960","DOI":"10.1016\/j.compbiomed.2023.106960","volume":"159","author":"H Zhang","year":"2023","unstructured":"Zhang H, Zhong X, Li G et al (2023) BCU-Net: bridging ConvNeXt and U-Net for medical image segmentation [J]. Comput Biol Med 159:106960","journal-title":"Comput Biol Med"},{"key":"1714_CR46","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.871","author":"J Huang","year":"2022","unstructured":"Huang J, Lin Z, Chen Y, Zhang X, Zhao W, Zhang J, Li Y, He X, Zhan M, Lu L, Jiang X, Peng Y (2022) DBFU-Net: double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel. Peer J Comput Sci. https:\/\/doi.org\/10.7717\/peerj-cs.871","journal-title":"Peer J Comput Sci"},{"key":"1714_CR47","doi-asserted-by":"publisher","first-page":"106626","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2023","unstructured":"Xu Q, Ma Z, Na H, Duan WJC (2023) DCSAU-Net: a deeper and more compact split-attention U-Net for medical image segmentation. Medicine 154:106626. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106626","journal-title":"Medicine"},{"key":"1714_CR48","doi-asserted-by":"publisher","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 764\u2013773. https:\/\/doi.org\/10.48550\/arXiv.1703.06211","DOI":"10.48550\/arXiv.1703.06211"},{"key":"1714_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103467","author":"X Deng","year":"2022","unstructured":"Deng X, Ye J (2022) A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2021.103467","journal-title":"Biomed Signal Process Control"},{"key":"1714_CR50","doi-asserted-by":"crossref","unstructured":"Qi Y, He Y, Qi X, Zhang Y, Yang G (2023) Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: 2023 IEEE\/CVF international conference on computer vision (ICCV), pp 6047\u20136056","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"1714_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108295","volume":"173","author":"J Liu","year":"2024","unstructured":"Liu J, Zhao D, Shen J, Geng P, Zhang Y, Yang J, Zhang Z (2024) HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features. Comput Biol Med 173:108295","journal-title":"Comput Biol Med"},{"key":"1714_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106","author":"TY Lin","year":"2017","unstructured":"Lin TY, Dollar P, Girshick R et al (2017) Feature pyramid networks for object detection [J]. IEEE Comput Soc. https:\/\/doi.org\/10.1109\/CVPR.2017.106","journal-title":"IEEE Comput Soc"},{"key":"1714_CR53","doi-asserted-by":"publisher","unstructured":"Ruan D, Wang D, Zheng Y, Zheng N, Zheng M (2021) Gaussian context transformer. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Nashville, TN, USA, pp 15124\u201315133. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01488","DOI":"10.1109\/CVPR46437.2021.01488"},{"key":"1714_CR54","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1714_CR55","unstructured":"Ghiasi G, Lin TY, Le QV (2018) Dropblock: a regularization method for convolutional networks [C]. In: Advances in neural information processing systems, pp 10727\u201310737"},{"key":"1714_CR56","doi-asserted-by":"crossref","unstructured":"Yu W, Luo M, Zhou P, Si C, Zhou Y, Wang X, Feng J, Yan S (2021) MetaFormer is actually what you need for vision. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 10809\u201310819","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"1714_CR57","doi-asserted-by":"crossref","unstructured":"Gonzalez-Jimenez A, Lionetti S, Gottfrois P, Groger F, Pouly M, Navarini AA (2023) Robust T-Loss for medical image segmentation. arXiv:2306.00753","DOI":"10.1007\/978-3-031-43898-1_68"},{"key":"1714_CR58","doi-asserted-by":"publisher","unstructured":"Staal J, Abramoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridgebased vessel segmentation in color images of the retina. IEEE Trans Med Imag 23(4):501\u2013509. https:\/\/drive.grand-challenge.org\/. https:\/\/doi.org\/10.1109\/TMI.2004.825627","DOI":"10.1109\/TMI.2004.825627"},{"key":"1714_CR59","doi-asserted-by":"publisher","unstructured":"Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imag 19(3):203\u2013210. http:\/\/cecas.clemson.edu\/~ahoover\/stare\/. https:\/\/doi.org\/10.1109\/42.845178.","DOI":"10.1109\/42.845178"},{"key":"1714_CR60","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1167\/iovs.08-3018","volume":"50","author":"CG Owen","year":"2009","unstructured":"Owen CG, Rudnicka AR, Mullen R, Barman SA, Monekosso D, Whincup PH, Paterson C (2009) Measuring retinal vessel tortuosity in 10-year- old children: validation of the computer- assisted image analysis of the retina (CAIAR) program. Invest Ophthalmol Vis Sci 50:2004\u20132010. https:\/\/doi.org\/10.1167\/iovs.08-3018","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"1714_CR61","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/154860","author":"A Budai","year":"2013","unstructured":"Budai A, Bock R, Maier AK, Hornegger J, Michelson G (2013) Robust vessel segmentation in fundus images. Int J Biomed Imag. https:\/\/doi.org\/10.1155\/2013\/154860","journal-title":"Int J Biomed Imag"},{"key":"1714_CR62","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. https:\/\/doi.org\/10.1016\/j.knosys.2019.04.025","journal-title":"Knowl Based Syst"},{"key":"1714_CR63","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neunet.2020.02.018","volume":"126","author":"Y Wu","year":"2020","unstructured":"Wu Y, Xia Y, Song Y, Zhang Y, Cai W (2020) NFN+: a novel network followed network for retinal vessel segmentation. Neural Netw 126:153\u2013162. https:\/\/doi.org\/10.1016\/j.neunet.2020.02.018","journal-title":"Neural Netw"},{"key":"1714_CR64","doi-asserted-by":"publisher","first-page":"3384","DOI":"10.1109\/JBHI.2020.3002985","volume":"24","author":"DY Wang","year":"2020","unstructured":"Wang DY, Ayman H, Jessica P, Osamah JS, Yang T (2020) Hard attention net for automatic retinal vessel segmentation. IEEE J Biomed Health Inf 24:3384\u20133396. https:\/\/doi.org\/10.1109\/JBHI.2020.3002985","journal-title":"IEEE J Biomed Health Inf"},{"key":"1714_CR65","doi-asserted-by":"publisher","unstructured":"Wang W, Zhong J, Wu H, Wen Z, Qin J (2020) RVSeg-Net: an efficient feature pyramid cascade network for retinal vessel segmentation, medical image computing and computer assisted intervention\u2014MICCAI, pp 796\u2013805. https:\/\/doi.org\/10.1007\/978-3-030-59722-1_77","DOI":"10.1007\/978-3-030-59722-1_77"},{"issue":"3","key":"1714_CR66","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TII.2020.2993842","volume":"17","author":"X Li","year":"2020","unstructured":"Li X, Jiang Y, Li M, Yin S (2020) Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans Industr Inf 17(3):1958\u20131967. https:\/\/doi.org\/10.1109\/TII.2020.2993842","journal-title":"IEEE Trans Industr Inf"},{"key":"1714_CR67","doi-asserted-by":"publisher","first-page":"5519","DOI":"10.1007\/s00500-020-05552-w","volume":"25","author":"C Wang","year":"2021","unstructured":"Wang C, Zhao Z, Yu Y (2021) Fine retinal vessel segmentation by combining Nest U-net and patch-learning. Soft Comput 25:5519\u20135532. https:\/\/doi.org\/10.1007\/s00500-020-05552-w","journal-title":"Soft Comput"},{"key":"1714_CR68","doi-asserted-by":"crossref","unstructured":"Cui Z, Song S, Chen L, Chen X, Qi J (2021) MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation","DOI":"10.21203\/rs.3.rs-627790\/v1"},{"issue":"22","key":"1714_CR69","doi-asserted-by":"publisher","first-page":"3810","DOI":"10.3390\/electronics11223810","volume":"11","author":"J Li","year":"2022","unstructured":"Li J, Gao G, Yang L, Liu Y, Yu H (2022) DEF-Net: a dual-encoder fusion network for fundus retinal vessel segmentation. Electronics 11(22):3810. https:\/\/doi.org\/10.3390\/electronics11223810","journal-title":"Electronics"},{"key":"1714_CR70","doi-asserted-by":"publisher","first-page":"118313","DOI":"10.1016\/j.eswa.2022.118313","volume":"209","author":"DE Alvarado-Carrillo","year":"2022","unstructured":"Alvarado-Carrillo DE, Dalmau-Cedeno OS (2022) Width attention based convolutional neural network for retinal vessel segmentation. Expert Syst Appl 209:118313. https:\/\/doi.org\/10.1016\/j.eswa.2022.118313","journal-title":"Expert Syst Appl"},{"key":"1714_CR71","doi-asserted-by":"publisher","unstructured":"Jha D, Smedsrud PH, Riegler MA, Johansen D, De Lange T, Halvorsen P, Johansen HD (2019) Resunet++: an advanced architecture for medical image segmentation. In: IEEE ISM, pp 225\u20132255. https:\/\/doi.org\/10.1109\/ISM46123.2019.00049","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"1714_CR72","doi-asserted-by":"publisher","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen YW, Wu J (2020) UNet 3+: a full-scale connected UNet for medical image segmentation, ICASSP 2020\u20142020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1055\u20131059. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"1714_CR73","doi-asserted-by":"publisher","first-page":"105769","DOI":"10.1016\/j.cmpb.2020.105769","volume":"198","author":"PM Samuel","year":"2021","unstructured":"Samuel PM, Veeramalai T, Net VSSC (2021) vessel specific skip chain convolutional network for blood vessel segmentation. Biomedicine 198:105769. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105769","journal-title":"Biomedicine"},{"key":"1714_CR74","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2019","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X, Liu W, Xiao B (2019) Deep high resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43:3349\u20133364. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983686","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1714_CR75","doi-asserted-by":"publisher","unstructured":"Mathews MR, Anzar SM, Krishnan RK (2020) EfficientNet for retinal blood vessel segmentation. In: 2020 3rd International conference on signal processing and information security, ICSPIS), pp1\u20134. https:\/\/doi.org\/10.1109\/ICSPIS51252.2020.9340135","DOI":"10.1109\/ICSPIS51252.2020.9340135"},{"key":"1714_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102025","volume":"70","author":"H Wu","year":"2021","unstructured":"Wu H, Wang W, Zhong J, Lei B, Wen Z, Qin J (2021) SCS-Net: a scale and context sensitive network for retinal vessel segmentation. Med Image Anal 70:102025. https:\/\/doi.org\/10.1016\/j.media.2021.102025","journal-title":"Med Image Anal"},{"key":"1714_CR77","doi-asserted-by":"publisher","unstructured":"Fu H, Cheng J, Xu Y, Wong DWK, Liu J, Cao X (2018) Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. In: IEEE transactions on medical imaging, TMI. https:\/\/doi.org\/10.1109\/TMI.2018.2791488","DOI":"10.1109\/TMI.2018.2791488"},{"key":"1714_CR78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-07750-0_17","author":"J Gurrola-Ramos","year":"2022","unstructured":"Gurrola-Ramos J, Cedeno OD, Alarcon TE (2022) Factorized U-net for retinal vessel segmentation. MCPR. https:\/\/doi.org\/10.1007\/978-3-031-07750-0_17","journal-title":"MCPR"},{"key":"1714_CR79","unstructured":"Zhuang J (2018) LadderNet: multi-path networks based on U-Net for medical image segmentation. arXiv:1810.07810"},{"key":"1714_CR80","doi-asserted-by":"crossref","unstructured":"Sun J, Darbeha F, Zaidi M, Wang B (2020) SAUNet: Shape attentive U-Net for interpretable medical image segmentation. arXiv:2001.07645","DOI":"10.1007\/978-3-030-59719-1_77"},{"key":"1714_CR81","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/JBHI.2021.3089201","volume":"26","author":"Y Yuan","year":"2022","unstructured":"Yuan Y, Zhang L, Wang L, Huang H (2022) Multi-level attention network for retinal vessel segmentation. IEEE J Biomed Health Inf 26:312\u2013323","journal-title":"IEEE J Biomed Health Inf"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01714-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01714-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01714-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:20:21Z","timestamp":1738268421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01714-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1714"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01714-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"25 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"114"}}