{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:15:43Z","timestamp":1779383743913,"version":"3.53.1"},"reference-count":67,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62131015"],"award-info":[{"award-number":["62131015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["21010502600"],"award-info":[{"award-number":["21010502600"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Key Research and Development Program of Guangdong Province, China","award":["2021B0101420006"],"award-info":[{"award-number":["2021B0101420006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1109\/tmi.2022.3225667","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:26:20Z","timestamp":1669854380000},"page":"1225-1236","source":"Crossref","is-referenced-by-count":24,"title":["BowelNet: Joint Semantic-Geometric Ensemble Learning for Bowel Segmentation From Both Partially and Fully Labeled CT Images"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0022-0217","authenticated-orcid":false,"given":"Chong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1415-177X","authenticated-orcid":false,"given":"Junwei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miaofei","family":"Han","sequence":"additional","affiliation":[{"name":"Shanghai United Imaging Intelligence Company Ltd., Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5571-6220","authenticated-orcid":false,"given":"Gustavo","family":"Carneiro","sequence":"additional","affiliation":[{"name":"Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7934-5698","authenticated-orcid":false,"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"issue":"1","key":"ref1","first-page":"19","article-title":"Genetic alterations in colorectal cancer","volume":"5","author":"Armaghany","year":"2012","journal-title":"Gastrointest. Cancer Res."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0b013e31818e4641"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/ijms18010197"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref5","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16437-8_2"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.8.002732"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1117\/12.2548910"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59719-1_21"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101896"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.863836"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/670739"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2271487"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICECDS.2017.8389744"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2898414"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66182-7_79"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00891"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.03.003"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14386"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01077"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00973"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59719-1_15"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3001036"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00125"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3025517"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6946"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.01.002"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/547897"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2013.10.028"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102386"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101851"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01629"},{"key":"ref36","first-page":"354","article-title":"Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!","volume-title":"Proc. 4th Conf. Med. Imag. Deep Learn.","author":"Kervadec"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2007.1002"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00543"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.01.015"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3030352"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015909"},{"key":"ref42","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. ICML Workshop","author":"Lee"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref45","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Paszke"},{"key":"ref46","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref47","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref50","first-page":"1","article-title":"Attention U-Net: Learning where to look for the pancreas","volume-title":"Proc. 1st Med. Imag. Deep Learn.","author":"Oktay"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"ref52","first-page":"1","article-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00864"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_24"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101884"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107762"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abfce3"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.03.026"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3090432"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680"},{"key":"ref62","article-title":"The KiTS 19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes","author":"Heller","year":"2019","journal-title":"arXiv:1904.00445"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2806309"},{"key":"ref64","article-title":"MICCAI multi-atlas labeling beyond the cranial vault\u2014Workshop and challenge","author":"Landman","year":"2015"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.07.036"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.916954"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10091712\/09966840.pdf?arnumber=9966840","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T03:08:54Z","timestamp":1706756934000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9966840\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":67,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2022.3225667","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4]]}}}