{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:10:42Z","timestamp":1760609442044,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82090052"],"award-info":[{"award-number":["82090052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology","award":["2021SHZDZX0103"],"award-info":[{"award-number":["2021SHZDZX0103"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1,7]]},"DOI":"10.1145\/3512388.3512420","type":"proceedings-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T02:28:13Z","timestamp":1648520893000},"page":"214-219","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SU-UNet: A Novel Self-Updating Network for Hepatic Vessel Segmentation in CT Images"],"prefix":"10.1145","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xukun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haopeng","family":"Kuang","sequence":"additional","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongwei","family":"Yang","sequence":"additional","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichao","family":"Yan","sequence":"additional","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhai","sequence":"additional","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy for Engineering&Technology, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"e_1_3_2_1_2_1","first-page":"166","article-title":"Image segmentation techniques: A survey","volume":"1","author":"Khan W.","year":"2013","unstructured":"Khan W. 2013 . Image segmentation techniques: A survey . Journal of Image and Graphics , 1 ( 4 ): 166 - 170 . http:\/\/www.joig.net\/uploadfile\/2013\/1226\/20131226051740869.pdf Khan W. 2013. Image segmentation techniques: A survey. Journal of Image and Graphics, 1(4): 166-170. http:\/\/www.joig.net\/uploadfile\/2013\/1226\/20131226051740869.pdf","journal-title":"Journal of Image and Graphics"},{"key":"e_1_3_2_1_3_1","unstructured":"Kaur Rupinder and Er Garima Malik. 2014. An image segmentation using improved FCM watershed algorithm and DBMF. Journal of Image and Graphics 2.2 (2014): 106-112. http:\/\/www.joig.net\/uploadfile\/2015\/0116\/20150116041324133.pdf  Kaur Rupinder and Er Garima Malik. 2014. An image segmentation using improved FCM watershed algorithm and DBMF. Journal of Image and Graphics 2.2 (2014): 106-112. http:\/\/www.joig.net\/uploadfile\/2015\/0116\/20150116041324133.pdf"},{"key":"e_1_3_2_1_4_1","article-title":"Effective Histogram Thresholding Techniques for Natural Images Using Segmentation","author":"Thanammal K K","year":"2014","unstructured":"Thanammal K K , Vijayalakshmi R R , Arumugaperumal S , 2014 . Effective Histogram Thresholding Techniques for Natural Images Using Segmentation . Journal of Image and Graphics, 2(2). Thanammal K K, Vijayalakshmi R R, Arumugaperumal S, 2014. Effective Histogram Thresholding Techniques for Natural Images Using Segmentation. Journal of Image and Graphics, 2(2).","journal-title":"Journal of Image and Graphics, 2(2)."},{"issue":"2","key":"e_1_3_2_1_5_1","first-page":"128","article-title":"Cancerous Masses Segmentation by Using Heuristic Ant Colony Algorithms in Medical Images","volume":"2","author":"Rezaee M","year":"2014","unstructured":"Rezaee M , Tavakoli M B . 2014 . Cancerous Masses Segmentation by Using Heuristic Ant Colony Algorithms in Medical Images . Journal of Image and Graphics , 2 ( 2 ): 128 . http:\/\/www.joig.org\/uploadfile\/2015\/0116\/20150116044800406.pdf Rezaee M, Tavakoli M B. 2014. Cancerous Masses Segmentation by Using Heuristic Ant Colony Algorithms in Medical Images. Journal of Image and Graphics, 2(2): 128. http:\/\/www.joig.org\/uploadfile\/2015\/0116\/20150116044800406.pdf","journal-title":"Journal of Image and Graphics"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Richard No\u00ebl 2013. Fuzzy color image segmentation using watershed transform. 157-160.  Richard No\u00ebl 2013. Fuzzy color image segmentation using watershed transform. 157-160.","DOI":"10.12720\/joig.1.3.157-160"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18178\/joig.8.1.21-25"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18178\/joig.7.4.134-139"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18178\/joig.8.2.42-46"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Wu J Li G Lu H 2021. A Supervoxel Classification Based Method for Multi-organ Segmentation from Abdominal CT Images. Journal of Image and Graphics 9(1). http:\/\/www.joig.net\/uploadfile\/2021\/0208\/20210208034617900.pdf  Wu J Li G Lu H 2021. A Supervoxel Classification Based Method for Multi-organ Segmentation from Abdominal CT Images. Journal of Image and Graphics 9(1). http:\/\/www.joig.net\/uploadfile\/2021\/0208\/20210208034617900.pdf","DOI":"10.18178\/joig.9.1.9-14"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0898-1221(98)00046-7"},{"key":"e_1_3_2_1_12_1","first-page":"1717","article-title":"A novel region growing method for segmenting ultrasound images. IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No. 00CH37121)","volume":"2","author":"Hao","year":"2000","unstructured":"Hao , Xiaohui , 2000 . A novel region growing method for segmenting ultrasound images. IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No. 00CH37121) . IEEE , 2 : 1717 - 1720 . https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/921653 Hao, Xiaohui, 2000. A novel region growing method for segmenting ultrasound images. IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No. 00CH37121). IEEE, 2: 1717-1720. https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/921653","journal-title":"IEEE"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Foruzan A H Zoroofi R A Sato Y 2012. A Hessian-based filter for vascular segmentation of noisy hepatic CT scans. International journal of computer assisted radiology and surgery 7(2): 199-205. https:\/\/linkspringer.fenshishang.com\/article\/10.1007\/s11548-011-0640-y  Foruzan A H Zoroofi R A Sato Y 2012. A Hessian-based filter for vascular segmentation of noisy hepatic CT scans. International journal of computer assisted radiology and surgery 7(2): 199-205. https:\/\/linkspringer.fenshishang.com\/article\/10.1007\/s11548-011-0640-y","DOI":"10.1007\/s11548-011-0640-y"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.08.018"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Xu Z Lu D Wang Y 2021.Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation. https:\/\/arxiv.fenshishang.com\/pdf\/2106.01860.pdf  Xu Z Lu D Wang Y 2021.Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation. https:\/\/arxiv.fenshishang.com\/pdf\/2106.01860.pdf","DOI":"10.1007\/978-3-030-87193-2_1"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa9262"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8802951"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00919-9_44"},{"key":"e_1_3_2_1_19_1","article-title":"An attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation","author":"Yan Q","year":"2020","unstructured":"Yan Q , Wang B , Zhang W , 2020 . An attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation . IEEE Journal of Biomedical and Health Informatics. https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/9277536 Yan Q, Wang B, Zhang W, 2020. An attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation. IEEE Journal of Biomedical and Health Informatics. https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/9277536","journal-title":"IEEE Journal of Biomedical and Health Informatics. https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/9277536"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18178\/joig.6.1.84-87"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.18178\/joig.7.3.94-101"},{"key":"e_1_3_2_1_22_1","unstructured":"Antonelli M Reinke A Bakas S 2021. The Medical Segmentation Decathlon. https:\/\/arxiv.fenshishang.com\/pdf\/2106.05735.pdf  Antonelli M Reinke A Bakas S 2021. The Medical Segmentation Decathlon. https:\/\/arxiv.fenshishang.com\/pdf\/2106.05735.pdf"},{"key":"e_1_3_2_1_23_1","volume-title":"Deep Learning Prediction of Incoming Rainfalls: An Operational Service for the City of Beijing China. 2019 International Conference on Data Mining Workshops (ICDMW). IEEE: 180-185","author":"Song K","year":"2019","unstructured":"Song K , Yang G , Wang Q , 2019 . Deep Learning Prediction of Incoming Rainfalls: An Operational Service for the City of Beijing China. 2019 International Conference on Data Mining Workshops (ICDMW). IEEE: 180-185 . https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/8955589 Song K, Yang G, Wang Q, 2019. Deep Learning Prediction of Incoming Rainfalls: An Operational Service for the City of Beijing China. 2019 International Conference on Data Mining Workshops (ICDMW). IEEE: 180-185. https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/8955589"},{"volume-title":"Weighted Res-UNet for High-Quality Retina Vessel Segmentation. 2018 9th International Conference on Information Technology in Medicine and Education (ITME)","author":"Xiao X","key":"e_1_3_2_1_24_1","unstructured":"Xiao X , Shen L , Luo Z , 2018. Weighted Res-UNet for High-Quality Retina Vessel Segmentation. 2018 9th International Conference on Information Technology in Medicine and Education (ITME) . IEEE Computer Society . https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/8589312 Xiao X, Shen L, Luo Z, 2018. Weighted Res-UNet for High-Quality Retina Vessel Segmentation. 2018 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE Computer Society. https:\/\/ieeexploreieee.fenshishang.com\/abstract\/document\/8589312"},{"key":"e_1_3_2_1_25_1","volume-title":"Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He K","year":"2016","unstructured":"He K , Zhang X , Ren S , 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE. https:\/\/openaccess.thecvf.com\/content_cvpr_ 2016 \/papers\/He_Deep_Residual_Learning_CVPR_2016_paper.pdf He K, Zhang X, Ren S, 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https:\/\/openaccess.thecvf.com\/content_cvpr_2016\/papers\/He_Deep_Residual_Learning_CVPR_2016_paper.pdf"},{"key":"e_1_3_2_1_26_1","volume-title":"Identity Mappings in Deep Residual Networks. Journal of European Conference on Computer Vision. https:\/\/linkspringer.fenshishang.com\/chapter\/10","author":"He K","year":"2016","unstructured":"He K , Zhang X , Ren S , 2016 . Identity Mappings in Deep Residual Networks. Journal of European Conference on Computer Vision. https:\/\/linkspringer.fenshishang.com\/chapter\/10 .1007\/978-3-319-46493-0_38 He K, Zhang X, Ren S, 2016. Identity Mappings in Deep Residual Networks. Journal of European Conference on Computer Vision. https:\/\/linkspringer.fenshishang.com\/chapter\/10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_1_27_1","volume-title":"Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction","author":"Fang X","year":"2001","unstructured":"Fang X , Yan P. Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction . Journal of IEEE Transactions on Medical Imaging , 39(11): 3619-3629. https:\/\/arxiv.fenshishang.com\/pdf\/ 2001 .00208.pdf Fang X, Yan P. Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. Journal of IEEE Transactions on Medical Imaging, 39(11): 3619-3629. https:\/\/arxiv.fenshishang.com\/pdf\/2001.00208.pdf"},{"key":"e_1_3_2_1_28_1","unstructured":"Chen S Ma K Zheng Y. 2019. Med3d: Transfer learning for 3d medical image analysis. https:\/\/arxiv.fenshishang.com\/pdf\/1904.00625.pdf  Chen S Ma K Zheng Y. 2019. Med3d: Transfer learning for 3d medical image analysis. https:\/\/arxiv.fenshishang.com\/pdf\/1904.00625.pdf"}],"event":{"name":"ICIGP 2022: 2022 the 5th International Conference on Image and Graphics Processing","acronym":"ICIGP 2022","location":"Beijing China"},"container-title":["2022 the 5th International Conference on Image and Graphics Processing (ICIGP)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512388.3512420","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3512388.3512420","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:43Z","timestamp":1750191103000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512388.3512420"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":28,"alternative-id":["10.1145\/3512388.3512420","10.1145\/3512388"],"URL":"https:\/\/doi.org\/10.1145\/3512388.3512420","relation":{},"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"2022-03-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}