{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T09:06:17Z","timestamp":1777626377997,"version":"3.51.4"},"publisher-location":"New York, New York, USA","reference-count":16,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1145\/3354031.3354050","type":"proceedings-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T12:34:07Z","timestamp":1569587647000},"page":"71-76","source":"Crossref","is-referenced-by-count":7,"title":["Retinal Artery\/Vein Classification via Rotation Augmentation and Deeply Supervised U-net Segmentation"],"prefix":"10.1145","author":[{"given":"Zhaolei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junbin","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weishi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/3354031.3354050-1","doi-asserted-by":"crossref","unstructured":"M. Miri, Z. Amini, H. Rabbani, and R. Kafieh. A comprehensive study of retinal vessel classification methods in fundus images. Journal of medical signals and sensors, 7(2):59, 2017.","DOI":"10.4103\/2228-7477.205505"},{"key":"key-10.1145\/3354031.3354050-2","doi-asserted-by":"crossref","unstructured":"M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. Van Ginneken, J. C. Folk, and M. D. Abramoff. Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. IEEE Transactions on medical imaging, 30(11):1941--1950, 2011.","DOI":"10.1109\/TMI.2011.2159619"},{"key":"key-10.1145\/3354031.3354050-3","doi-asserted-by":"crossref","unstructured":"B. Dashtbozorg, A. M. Mendon&#231;a, and A. Campilho. An automatic graph-based approach for artery\/vein classification in retinal images. IEEE Transactions on Image Processing, 23(3):1073--1083, 2014.","DOI":"10.1109\/TIP.2013.2263809"},{"key":"key-10.1145\/3354031.3354050-4","doi-asserted-by":"crossref","unstructured":"R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu. Retinal artery-vein classification via topology estimation. IEEE transactions on medical imaging, 34(12):2518--2534, 2015.","DOI":"10.1109\/TMI.2015.2443117"},{"key":"key-10.1145\/3354031.3354050-5","doi-asserted-by":"crossref","unstructured":"Y. Zhao, J. Xie, P. Su, Y. Zheng, Y. Liu, J. Cheng, and J. Liu. Retinal artery and vein classification via dominant sets clustering-based vascular topology estimation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 56--64, 2018.","DOI":"10.1007\/978-3-030-00934-2_7"},{"key":"key-10.1145\/3354031.3354050-6","doi-asserted-by":"crossref","unstructured":"Q. Mirsharif, F. Tajeripour, and H. Pourreza. Automated characterization of blood vessels as arteries and veins in retinal images. Computerized Medical Imaging and Graphics, 37(7-8):607--617, 2013.","DOI":"10.1016\/j.compmedimag.2013.06.003"},{"key":"key-10.1145\/3354031.3354050-7","doi-asserted-by":"crossref","unstructured":"F. Huang, B. Dashtbozorg, and B. M. ter Haar Romeny. Artery\/vein classification using reflection features in retina fundus images. Machine Vision and Applications, 29(1):23--34, 2018.","DOI":"10.1007\/s00138-017-0867-x"},{"key":"key-10.1145\/3354031.3354050-8","doi-asserted-by":"crossref","unstructured":"F. Huang, B. Dashtbozorg, T. Tan, and B. M. ter Haar Romeny. Retinal artery\/vein classification using genetic-search feature selection. Computer methods and programs in biomedicine, 161:197--207, 2018.","DOI":"10.1016\/j.cmpb.2018.04.016"},{"key":"key-10.1145\/3354031.3354050-9","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234--241, 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"key-10.1145\/3354031.3354050-10","doi-asserted-by":"crossref","unstructured":"J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431--3440, 2015.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"key-10.1145\/3354031.3354050-11","doi-asserted-by":"crossref","unstructured":"P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D'Anastasi, et al. Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 415--423. Springer, 2016.","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"key-10.1145\/3354031.3354050-12","doi-asserted-by":"crossref","unstructured":"Y. Wu, Y. Xia, Y. Song, Y. Zhang, and W. Cai. Multiscale network followed network model for retinal vessel segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 119--126, 2018.","DOI":"10.1007\/978-3-030-00934-2_14"},{"key":"key-10.1145\/3354031.3354050-13","unstructured":"C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu. Deeply-supervised nets. In Artificial Intelligence and Statistics, pages 562--570, 2015."},{"key":"key-10.1145\/3354031.3354050-14","doi-asserted-by":"crossref","unstructured":"Y. Zhang and A. C. Chung. Deep supervision with additional labels for retinal vessel segmentation task. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 83--91, 2018.","DOI":"10.1007\/978-3-030-00934-2_10"},{"key":"key-10.1145\/3354031.3354050-15","doi-asserted-by":"crossref","unstructured":"J. Staal, M. D. Abr&#224;moff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4):501--509, 2004.","DOI":"10.1109\/TMI.2004.825627"},{"key":"key-10.1145\/3354031.3354050-16","doi-asserted-by":"crossref","unstructured":"Q. Hu, M. D. Abr&#224;moff, and M. K. Garvin. Automated separation of binary overlapping trees in low-contrast color retinal images. In International conference on medical image computing and computer-assisted intervention, pages 436--443, 2013.","DOI":"10.1007\/978-3-642-40763-5_54"}],"event":{"name":"the 2019 4th International Conference","location":"Chengdu, China","acronym":"ICBIP '19","number":"4","sponsor":["Graduate School of Library, Information, and Media Studies, University of Tsukuba, Japan","Sichuan University"],"start":{"date-parts":[[2019,8,13]]},"end":{"date-parts":[[2019,8,15]]}},"container-title":["Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019)  - ICBIP '19"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3354031.3354050","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3354050&ftid=2085628&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:55Z","timestamp":1750203895000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3354031.3354050"}},"subtitle":[],"proceedings-subject":"Biomedical Signal and Image Processing (ICBIP 2019)","short-title":[],"issued":{"date-parts":[[2019]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1145\/3354031.3354050","relation":{},"subject":[],"published":{"date-parts":[[2019]]}}}