{"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/deep-vessel-segmentation-by-learning","title":"Deep Vessel Segmentation By Learning Graphical Connectivity","arxiv_id":"1806.02279","date":"2018-06-06","proceeding":null,"authors":["Seung Yeon Shin","Soochahn Lee","Il Dong Yun","Kyoung Mu Lee"],"abstract":"We propose a novel deep-learning-based system for vessel segmentation.\nExisting methods using CNNs have mostly relied on local appearances learned on\nthe regular image grid, without considering the graphical structure of vessel\nshape. To address this, we incorporate a graph convolutional network into a\nunified CNN architecture, where the final segmentation is inferred by combining\nthe different types of features. The proposed method can be applied to expand\nany type of CNN-based vessel segmentation method to enhance the performance.\nExperiments show that the proposed method outperforms the current\nstate-of-the-art methods on two retinal image datasets as well as a coronary\nartery X-ray angiography dataset.","url_abs":"http://arxiv.org/abs/1806.02279v1","url_pdf":"http://arxiv.org/pdf/1806.02279v1.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":[{"paper_slug":"deep-vessel-segmentation-by-learning","repo_url":"https://github.com/syshin1014/VGN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"retinal-vessel-segmentation","task_name":"Retinal Vessel Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/retinal-vessel-segmentation-on-chase_db1","task":"Retinal Vessel Segmentation","dataset":"CHASE_DB1","model":"VGN","rank_in_archive_order":8,"of":16,"metrics":{"AUC":"0.9830","F1 score":"0.8034"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-drive","task":"Retinal Vessel Segmentation","dataset":"DRIVE","model":"VGN","rank_in_archive_order":10,"of":22,"metrics":{"AUC":"0.9802","F1 score":"0.8263"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-hrf","task":"Retinal Vessel Segmentation","dataset":"HRF","model":"VGN","rank_in_archive_order":3,"of":4,"metrics":{"AUC":"0.9838","F1 score":"0.8151"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-stare","task":"Retinal Vessel Segmentation","dataset":"STARE","model":"VGN","rank_in_archive_order":5,"of":10,"metrics":{"AUC":"0.9877","F1 score":"0.8429"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02279","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}