{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:07:20Z","timestamp":1767262040187,"version":"3.40.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"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":["62271220","62202179"],"award-info":[{"award-number":["62271220","62202179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Fund of Shenzhen","award":["2024534319"],"award-info":[{"award-number":["2024534319"]}]},{"DOI":"10.13039\/501100001809","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["HUST: 2024JYCXJJ032"],"award-info":[{"award-number":["HUST: 2024JYCXJJ032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1109\/tmi.2024.3493456","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T19:54:07Z","timestamp":1731009247000},"page":"1386-1399","source":"Crossref","is-referenced-by-count":5,"title":["SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8291-4823","authenticated-orcid":false,"given":"Xian","family":"Lin","sequence":"first","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Yangyang","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5249-4171","authenticated-orcid":false,"given":"Zhehao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3885-4912","authenticated-orcid":false,"given":"Kwang-Ting","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2039-3863","authenticated-orcid":false,"given":"Zengqiang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5060-2558","authenticated-orcid":false,"given":"Li","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.242025724"},{"key":"ref2","article-title":"SAM-Med2D","author":"Cheng","year":"2023","journal-title":"arXiv:2308.16184"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-05881-4"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918"},{"key":"ref8","article-title":"Computer-vision benchmark segment-anything model (SAM) in medical images: Accuracy in 12 datasets","author":"He","year":"2023","journal-title":"arXiv:2304.09324"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103061"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"ref11","article-title":"Customized segment anything model for medical image segmentation","author":"Zhang","year":"2023","journal-title":"arXiv:2304.13785"},{"key":"ref12","article-title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","author":"Wu","year":"2023","journal-title":"arXiv:2304.12620"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1117\/12.3006809"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3226268"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3230943"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.04306"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110491"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72111-3_3"},{"key":"ref20","article-title":"AutoProSAM: Automated prompting SAM for 3D multi-organ segmentation","author":"Li","year":"2023","journal-title":"arXiv:2308.14936"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28514"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM58861.2023.10385570"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i20.30260"},{"key":"ref24","article-title":"AutoSAM: Adapting SAM to medical images by overloading the prompt encoder","author":"Shaharabany","year":"2023","journal-title":"arXiv:2306.06370"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01074"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/data5010014"},{"volume-title":"Lung CT Segmentation Challenge 2017","year":"2020","author":"Yang","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"ref29","article-title":"The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge","author":"Li","year":"2023","journal-title":"arXiv:2301.03281"},{"volume-title":"COVID-19 CT Images Segmentation","year":"2020","author":"Slinko","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101950"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16197"},{"key":"ref33","article-title":"LNDb: A lung nodule database on computed tomography","author":"Pedrosa","year":"2019","journal-title":"arXiv:1911.08434"},{"key":"ref34","article-title":"TotalSegmentator: Robust segmentation of 104 anatomical structures in CT images","author":"Wasserthal","year":"2022","journal-title":"arXiv:2208.05868"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102957"},{"key":"ref36","article-title":"The KiTS21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT","author":"Heller","year":"2023","journal-title":"arXiv:2307.01984"},{"key":"ref37","first-page":"4","article-title":"Automatic structure segmentation for radio therapy planning challenge 2020","volume-title":"Proc. MICCAI","author":"Li"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15485"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12197"},{"volume-title":"Pancreas-CT","year":"2020","author":"Roth","key":"ref40"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102605"},{"key":"ref42","article-title":"AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation","author":"Ji","year":"2022","journal-title":"arXiv:2206.08023"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14676"},{"key":"ref44","article-title":"MosMedData: Chest CT scans with COVID-19 related findings dataset","author":"Morozov","year":"2020","journal-title":"arXiv:2005.06465"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102642"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2020.01.012"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101537"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.7303\/SYN3193805"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.180"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2983721"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3035253"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3264513"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3293771"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"ref60","article-title":"Uncertainty-aware adapter: Adapting segment anything model (SAM) for ambiguous medical image segmentation","author":"Jiang","year":"2024","journal-title":"arXiv:2403.10931"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01216"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3332944"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_59"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11756"},{"key":"ref65","article-title":"A hierarchical probabilistic U-Net for modeling multi-scale ambiguities","author":"Kohl","year":"2019","journal-title":"arXiv:1905.13077"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/10930313\/10746534.pdf?arnumber=10746534","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T17:37:08Z","timestamp":1742319428000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10746534\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":65,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2024.3493456","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"type":"print","value":"0278-0062"},{"type":"electronic","value":"1558-254X"}],"subject":[],"published":{"date-parts":[[2025,3]]}}}