{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:45:53Z","timestamp":1769856353011,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62073325 62003343 62222316 and U20A20224"],"award-info":[{"award-number":["62073325 62003343 62222316 and U20A20224"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFB4700902"],"award-info":[{"award-number":["2022YFB4700902"]}]},{"name":"National High Level Hospital Clinical Research Funding","award":["2022-PUMCH-B-125"],"award-info":[{"award-number":["2022-PUMCH-B-125"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3613766","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:30Z","timestamp":1698391650000},"page":"2035-2044","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards Flexible and Universal: A Novel Endpoint-based Framework for Vessel Structural Information Extraction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5411-4527","authenticated-orcid":false,"given":"Xiyao","family":"Ma","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences &amp; Institute of Automation, Chinese Academy of Science, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-8448","authenticated-orcid":false,"given":"Shiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Science &amp; Huadong Hospital Affiliated to Fudan University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6227-4811","authenticated-orcid":false,"given":"Xiaoliang","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7602-4848","authenticated-orcid":false,"given":"Xiaohu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1534-5840","authenticated-orcid":false,"given":"Zengguang","family":"Hou","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5000-6721","authenticated-orcid":false,"given":"Xinkai","family":"Qu","sequence":"additional","affiliation":[{"name":"Huadong Hospital Affiliated to Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0998-6729","authenticated-orcid":false,"given":"Wenzheng","family":"Han","sequence":"additional","affiliation":[{"name":"Huadong Hospital Affiliated to Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3704-6067","authenticated-orcid":false,"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"Huadong Hospital Affiliated to Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4943-0706","authenticated-orcid":false,"given":"Meng","family":"Song","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8954-3498","authenticated-orcid":false,"given":"Linsen","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2021. Cardiovascular diseases (CVDs). https:\/\/www.who.int\/news-room\/fact- sheets\/detail\/cardiovascular-diseases-(cvds)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG47880.2020.00014"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_27"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_3_2_1_6_1","volume-title":"Sergio Solorio-Meza, Manuel Ornelas-Rodriguez, and Miguel Torres-Cisneros.","author":"Cervantes-Sanchez Fernando","year":"2016","unstructured":"Fernando Cervantes-Sanchez, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Juan Gabriel Avi na-Cervantes, Sergio Solorio-Meza, Manuel Ornelas-Rodriguez, and Miguel Torres-Cisneros. 2016. Segmentation of coronary angiograms using Gabor filters and Boltzmann univariate marginal distribution algorithm. Computational Intelligence and Neuroscience (2016)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00590"},{"key":"e_1_3_2_1_8_1","volume-title":"Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306","author":"Chen Jieneng","year":"2021","unstructured":"Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou. 2021. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00543"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.510"},{"key":"e_1_3_2_1_11_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00238"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0056195"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00326"},{"key":"e_1_3_2_1_15_1","volume-title":"Beyond self-attention: External attention using two linear layers for visual tasks","author":"Guo Meng-Hao","year":"2022","unstructured":"Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, and Shi-Min Hu. 2022a. Beyond self-attention: External attention using two linear layers for visual tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_1_16_1","volume-title":"Visual attention network. arXiv preprint arXiv:2202.09741","author":"Guo Meng-Hao","year":"2022","unstructured":"Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, and Shi-Min Hu. 2022b. Visual attention network. arXiv preprint arXiv:2202.09741 (2022)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00069"},{"key":"e_1_3_2_1_21_1","volume-title":"A region growing vessel segmentation algorithm based on spectrum information. Computational and mathematical methods in medicine","author":"Jiang Huiyan","year":"2013","unstructured":"Huiyan Jiang, Baochun He, Di Fang, Zhiyuan Ma, Benqiang Yang, and Libo Zhang. 2013. A region growing vessel segmentation algorithm based on spectrum information. Computational and mathematical methods in medicine (2013)."},{"key":"e_1_3_2_1_22_1","volume-title":"Minimally invasive cardiovascular surgery: incisions and approaches. Methodist DeBakey cardiovascular journal","author":"Langer Nathaniel B","year":"2016","unstructured":"Nathaniel B Langer and Michael Argenziano. 2016. Minimally invasive cardiovascular surgery: incisions and approaches. Methodist DeBakey cardiovascular journal, Vol. 12, 1 (2016), 4."},{"key":"e_1_3_2_1_23_1","volume-title":"Rethinking on multi-stage networks for human pose estimation. arXiv preprint arXiv:1901.00148","author":"Li Wenbo","year":"2019","unstructured":"Wenbo Li, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei, and Jian Sun. 2019b. Rethinking on multi-stage networks for human pose estimation. arXiv preprint arXiv:1901.00148 (2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00060"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01112"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00546"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00744"},{"key":"e_1_3_2_1_29_1","unstructured":"Volodymyr Mnih Nicolas Heess Alex Graves et al. 2014. Recurrent models of visual attention. Advances in neural information processing systems Vol. 27 (2014)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"e_1_3_2_1_31_1","volume-title":"Bam: Bottleneck attention module. arXiv preprint arXiv:1807.06514","author":"Park Jongchan","year":"2018","unstructured":"Jongchan Park, Sanghyun Woo, Joon-Young Lee, and In So Kweon. 2018. Bam: Bottleneck attention module. arXiv preprint arXiv:1807.06514 (2018)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2999257"},{"key":"e_1_3_2_1_33_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234--241."},{"key":"e_1_3_2_1_34_1","volume-title":"Amir Hossein Foruzan, and Ardeshir Dolati","author":"Sangsefidi Neda","year":"2018","unstructured":"Neda Sangsefidi, Amir Hossein Foruzan, and Ardeshir Dolati. 2018. Balancing the data term of graph-cuts algorithm to improve segmentation of hepatic vascular structures. Computers in biology and medicine, Vol. 93 (2018), 117--126."},{"key":"e_1_3_2_1_35_1","first-page":"219","article-title":"The SYNTAX Score: an angiographic tool grading the complexity of coronary artery disease","volume":"1","author":"Sianos Georgios","year":"2005","unstructured":"Georgios Sianos, Marie-Ang\u00e8le Morel, Arie Pieter Kappetein, Marie-Claude Morice, Antonio Colombo, Keith Dawkins, Marcel van den Brand, Nic Van Dyck, Mary E Russell, Friedrich W Mohr, et al. 2005. The SYNTAX Score: an angiographic tool grading the complexity of coronary artery disease. EuroIntervention, Vol. 1, 2 (2005), 219--227.","journal-title":"EuroIntervention"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00584"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3161681"},{"key":"e_1_3_2_1_38_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ?ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58548-8_7"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2983686"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00707"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01584"},{"key":"e_1_3_2_1_45_1","volume-title":"Vitpose: Simple vision transformer baselines for human pose estimation. arXiv preprint arXiv:2204.12484","author":"Xu Yufei","year":"2022","unstructured":"Yufei Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao. 2022. Vitpose: Simple vision transformer baselines for human pose estimation. arXiv preprint arXiv:2204.12484 (2022)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.253"},{"key":"e_1_3_2_1_47_1","first-page":"7281","article-title":"Hrformer: High-resolution vision transformer for dense predict","volume":"34","author":"Yuan Yuhui","year":"2021","unstructured":"Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang. 2021. Hrformer: High-resolution vision transformer for dense predict. Advances in Neural Information Processing Systems, Vol. 34 (2021), 7281--7293.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_48_1","volume-title":"Automatic liver vessel segmentation using 3D region growing and hybrid active contour model. Computers in biology and medicine","author":"Tang Ping","year":"2018","unstructured":"Ye-zhan Zeng, Sheng-hui Liao, Ping Tang, Yu-qian Zhao, Miao Liao, Yan Chen, and Yi-xiong Liang. 2018. Automatic liver vessel segmentation using 3D region growing and hybrid active contour model. Computers in biology and medicine, Vol. 97 (2018), 63--73."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"e_1_3_2_1_50_1","volume-title":"International conference on machine learning. PMLR, 7354--7363","author":"Zhang Han","year":"2019","unstructured":"Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019a. Self-attention generative adversarial networks. In International conference on machine learning. PMLR, 7354--7363."},{"key":"e_1_3_2_1_51_1","volume-title":"Human pose estimation with spatial contextual information. arXiv preprint arXiv:1901.01760","author":"Zhang Hong","year":"2019","unstructured":"Hong Zhang, Hao Ouyang, Shu Liu, Xiaojuan Qi, Xiaoyong Shen, Ruigang Yang, and Jiaya Jia. 2019b. Human pose estimation with spatial contextual information. arXiv preprint arXiv:1901.01760 (2019)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_35"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103472"},{"key":"e_1_3_2_1_54_1","volume-title":"Transfuse: Fusing transformers and cnns for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference","author":"Zhang Yundong","year":"2021","unstructured":"Yundong Zhang, Huiye Liu, and Qiang Hu. 2021a. Transfuse: Fusing transformers and cnns for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part I 24. Springer, 14--24."}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3613766","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3613766","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:04:41Z","timestamp":1755821081000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3613766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":54,"alternative-id":["10.1145\/3581783.3613766","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3613766","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}