{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dunet-a-deformable-network-for-retinal-vessel","title":"DUNet: A deformable network for retinal vessel segmentation","arxiv_id":"1811.01206","date":"2018-11-03","proceeding":null,"authors":["Qiangguo Jin","Zhaopeng Meng","Tuan D. Pham","Qi Chen","Leyi Wei","Ran Su"],"abstract":"Automatic segmentation of retinal vessels in fundus images plays an important\nrole in the diagnosis of some diseases such as diabetes and hypertension. In\nthis paper, we propose Deformable U-Net (DUNet), which exploits the retinal\nvessels' local features with a U-shape architecture, in an end to end manner\nfor retinal vessel segmentation. Inspired by the recently introduced deformable\nconvolutional networks, we integrate the deformable convolution into the\nproposed network. The DUNet, with upsampling operators to increase the output\nresolution, is designed to extract context information and enable precise\nlocalization by combining low-level feature maps with high-level ones.\nFurthermore, DUNet captures the retinal vessels at various shapes and scales by\nadaptively adjusting the receptive fields according to vessels' scales and\nshapes. Three public datasets DRIVE, STARE and CHASE_DB1 are used to train and\ntest our model. Detailed comparisons between the proposed network and the\ndeformable neural network, U-Net are provided in our study. Results show that\nmore detailed vessels are extracted by DUNet and it exhibits state-of-the-art\nperformance for retinal vessel segmentation with a global accuracy of\n0.9697/0.9722/0.9724 and AUC of 0.9856/0.9868/0.9863 on DRIVE, STARE and\nCHASE_DB1 respectively. Moreover, to show the generalization ability of the\nDUNet, we used another two retinal vessel data sets, one is named WIDE and the\nother is a synthetic data set with diverse styles, named SYNTHE, to\nqualitatively and quantitatively analyzed and compared with other methods.\nResults indicates that DUNet outperforms other state-of-the-arts.","url_abs":"http://arxiv.org/abs/1811.01206v1","url_pdf":"http://arxiv.org/pdf/1811.01206v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"retinal-vessel-segmentation","task_name":"Retinal Vessel Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deformable-convolution","method_name":"Deformable Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/retinal-vessel-segmentation-on-chase_db1","task":"Retinal Vessel Segmentation","dataset":"CHASE_DB1","model":"DUNet","rank_in_archive_order":11,"of":16,"metrics":{"AUC":"0.9804","F1 score":"0.7883"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-drive","task":"Retinal Vessel Segmentation","dataset":"DRIVE","model":"DUNet","rank_in_archive_order":11,"of":22,"metrics":{"AUC":"0.9802","F1 score":"0.8237"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-stare","task":"Retinal Vessel Segmentation","dataset":"STARE","model":"DUNet","rank_in_archive_order":7,"of":10,"metrics":{"AUC":"0.9832","F1 score":"0.8143"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.01206","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}