{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:41:37Z","timestamp":1768444897381,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61962054"],"award-info":[{"award-number":["61962054"]}],"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":["61163036"],"award-info":[{"award-number":["61163036"]}],"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":["1606RJZA047"],"award-info":[{"award-number":["1606RJZA047"]}],"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":["nwnu-kjcxgc-03-67"],"award-info":[{"award-number":["nwnu-kjcxgc-03-67"]}],"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":["NWNU-LKZD2021-06"],"award-info":[{"award-number":["NWNU-LKZD2021-06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of China","award":["61962054"],"award-info":[{"award-number":["61962054"]}]},{"name":"Natural Science Foundation of China","award":["61163036"],"award-info":[{"award-number":["61163036"]}]},{"name":"Natural Science Foundation of China","award":["1606RJZA047"],"award-info":[{"award-number":["1606RJZA047"]}]},{"name":"Natural Science Foundation of China","award":["nwnu-kjcxgc-03-67"],"award-info":[{"award-number":["nwnu-kjcxgc-03-67"]}]},{"name":"Natural Science Foundation of China","award":["NWNU-LKZD2021-06"],"award-info":[{"award-number":["NWNU-LKZD2021-06"]}]},{"name":"Northwest Normal University","award":["61962054"],"award-info":[{"award-number":["61962054"]}]},{"name":"Northwest Normal University","award":["61163036"],"award-info":[{"award-number":["61163036"]}]},{"name":"Northwest Normal University","award":["1606RJZA047"],"award-info":[{"award-number":["1606RJZA047"]}]},{"name":"Northwest Normal University","award":["nwnu-kjcxgc-03-67"],"award-info":[{"award-number":["nwnu-kjcxgc-03-67"]}]},{"name":"Northwest Normal University","award":["NWNU-LKZD2021-06"],"award-info":[{"award-number":["NWNU-LKZD2021-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Accurate medical imaging segmentation of the retinal fundus vasculature is essential to assist physicians in diagnosis and treatment. In recent years, convolutional neural networks (CNN) are widely used to classify retinal blood vessel pixels for retinal blood vessel segmentation tasks. However, the convolutional block receptive field is limited, simple multiple superpositions tend to cause information loss, and there are limitations in feature extraction as well as vessel segmentation. To address these problems, this paper proposes a new retinal vessel segmentation network based on U-Net, which is called multi-scale cross-position attention network (MCPANet). MCPANet uses multiple scales of input to compensate for image detail information and applies to skip connections between encoding blocks and decoding blocks to ensure information transfer while effectively reducing noise. We propose a cross-position attention module to link the positional relationships between pixels and obtain global contextual information, which enables the model to segment not only the fine capillaries but also clear vessel edges. At the same time, multiple scale pooling operations are used to expand the receptive field and enhance feature extraction. It further reduces pixel classification errors and eases the segmentation difficulty caused by the asymmetry of fundus blood vessel distribution. We trained and validated our proposed model on three publicly available datasets, DRIVE, CHASE, and STARE, which obtained segmentation accuracy of 97.05%, 97.58%, and 97.68%, and Dice of 83.15%, 81.48%, and 85.05%, respectively. The results demonstrate that the proposed method in this paper achieves better results in terms of performance and segmentation results when compared with existing methods.<\/jats:p>","DOI":"10.3390\/sym14071357","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"1357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3034-6729","authenticated-orcid":false,"given":"Yun","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4765-2626","authenticated-orcid":false,"given":"Jing","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongtong","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1865-1680","authenticated-orcid":false,"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8852-712X","authenticated-orcid":false,"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinkun","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"795","DOI":"10.4103\/1673-5374.322457","article-title":"Diabetic retinopathy: Neurovascular disease requiring neuroprotective and regenerative therapies","volume":"17","author":"Oshitari","year":"2022","journal-title":"Neural Regen. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101905","DOI":"10.1016\/j.media.2020.101905","article-title":"A review of machine learning methods for retinal blood vessel segmentation and artery\/vein classification","volume":"68","author":"Mookiah","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_3","first-page":"1118","article-title":"Blood vessel segmentation of fundus images by major vessel extraction and subimage classification","volume":"19","author":"Roychowdhury","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.eswa.2018.06.034","article-title":"Retinal vessel segmentation based on fully convolutional neural networks","volume":"112","author":"Oliveira","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11634-016-0257-7","article-title":"Multi-Objective retinal vessel localization using flower pollination search algorithm with pattern search","volume":"11","author":"Emary","year":"2017","journal-title":"Adv. Data Anal. Classif."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.eswa.2017.02.015","article-title":"An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images","volume":"78","author":"Neto","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"22337","DOI":"10.1007\/s11042-020-08958-8","article-title":"A novel automatic retinal vessel extraction using maximum entropy based EM algorithm","volume":"79","author":"Jainish","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","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":"ref_9","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/TMI.2020.3035253","article-title":"CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation","volume":"40","author":"Gu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","article-title":"Multi-Level deep supervised networks for retinal vessel segmentation","volume":"12","author":"Mo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_15","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., and Li, S. (2018, January 9\u201321). Weighted res-unet for high-quality retina vessel segmentation. Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00080"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-Local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1167\/iovs.08-3018","article-title":"Measuring retinal vessel tortuosity in 10-year-old children: Validation of the computer-assisted image analysis of the retina(CAIAR) program","volume":"50","author":"Owen","year":"2009","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_22","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 691filter response","volume":"19","author":"Hoover","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhang, H., Tan, N., and Chen, L. (2019). Automatic retinal blood vessel segmentation based on fully convolutional neural networks. Symmetry, 11.","DOI":"10.3390\/sym11091112"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.media.2014.08.002","article-title":"Trainable COSFIRE filters for vessel delineation with application to retinal images","volume":"19","author":"Azzopardi","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1263056","DOI":"10.1155\/2017\/1263056","article-title":"Retina image vessel segmentation using a hybrid CGLI level set method","volume":"2017","author":"Chen","year":"2017","journal-title":"Biomed. Res. Int."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018). Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5146","DOI":"10.1007\/s10489-020-01966-z","article-title":"SAT-Net: A side attention network for retinal image segmentation","volume":"51","author":"Tong","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.bbe.2020.01.011","article-title":"Multi-Path convolutional neural network in fundus segmentation of blood vessels","volume":"40","author":"Tian","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_29","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":"ref_30","doi-asserted-by":"crossref","first-page":"32826","DOI":"10.1109\/ACCESS.2020.2974027","article-title":"Attention guided u-net with atrous convolution for accurate retinal vessels segmentation","volume":"8","author":"Lv","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhong, J., Wu, H., Wen, Z., and Qin, J. (2020, January 4\u20138). Rvseg-Net: An efficient feature pyramid cascade network for retinal vessel segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59722-1_77"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guo, C., Szemenyei, M., Yi, Y., Zhou, W., and Bian, H. (2020, January 18\u201322). Residual Spatial Attention Network for Retinal Vessel Segmentation. Proceedings of the International Conference on Neural Information Processing, Bangkok, Thailand.","DOI":"10.1007\/978-3-030-63830-6_43"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s12539-020-00385-5","article-title":"Fully automatic arteriovenous segmentation in retinal images via topology-aware generative adversarial networks","volume":"12","author":"Yang","year":"2020","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"146308","DOI":"10.1109\/ACCESS.2020.3015108","article-title":"M-Gan: Retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks","volume":"8","author":"Park","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.media.2021.102025","article-title":"SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation","volume":"70","author":"Wu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., and Fan, C. (2021, January 10\u201315). Sa-Unet: Spatial attention u-net for retinal vessel segmentation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"117009","DOI":"10.1016\/j.eswa.2022.117009","article-title":"Detecting retinal vasculature as a key biomarker for deep Learning-based intelligent screening and analysis of diabetic and hypertensive retinopathy","volume":"200","author":"Arsalan","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, L., Verma, M., Nakashima, Y., Nagahara, H., and Kawasaki, R. (2020, January 1\u20135). Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV45572.2020.9093621"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-Net: Context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/7\/1357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:34Z","timestamp":1760139694000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/7\/1357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["sym14071357"],"URL":"https:\/\/doi.org\/10.3390\/sym14071357","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]}}}