{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T22:47:43Z","timestamp":1774651663505,"version":"3.50.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"NSFC Project","award":["62222604"],"award-info":[{"award-number":["62222604"]}]},{"name":"NSFC Project","award":["62206052"],"award-info":[{"award-number":["62206052"]}]},{"name":"China Post-Doctoral Science Foundation","award":["2024M750424"],"award-info":[{"award-number":["2024M750424"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["020214380120"],"award-info":[{"award-number":["020214380120"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["020214380128"],"award-info":[{"award-number":["020214380128"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory Fund","award":["ZZKT2024A14"],"award-info":[{"award-number":["ZZKT2024A14"]}]},{"name":"Post-Doctoral Fellowship Programof China Postdoctoral Science Foundation","award":["GZC20240252"],"award-info":[{"award-number":["GZC20240252"]}]},{"name":"Jiangsu Funding Program for Excellent Post-Doctoral Talent","award":["2024ZB242"],"award-info":[{"award-number":["2024ZB242"]}]},{"name":"Jiangsu Science and Technology Major Project","award":["BG2024031"],"award-info":[{"award-number":["BG2024031"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023MF037"],"award-info":[{"award-number":["ZR2023MF037"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Open Project Program of the State Key Laboratory of CAD&CG, Zhejiang University","doi-asserted-by":"publisher","award":["A2320"],"award-info":[{"award-number":["A2320"]}],"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":[[2025,10]]},"DOI":"10.1109\/tmi.2025.3532084","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T13:50:57Z","timestamp":1737381057000},"page":"3909-3923","source":"Crossref","is-referenced-by-count":15,"title":["Stitching, Fine-Tuning, and Re-Training: A SAM-Enabled Framework for Semi-Supervised 3D Medical Image Segmentation"],"prefix":"10.1109","volume":"44","author":[{"given":"Shumeng","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the National Institute of Healthcare Data Science, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-0702","authenticated-orcid":false,"given":"Lei","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9981-3738","authenticated-orcid":false,"given":"Qian","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Shandong Women’s University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-455X","authenticated-orcid":false,"given":"Jing","family":"Huo","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the National Institute of Healthcare Data Science, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-7318","authenticated-orcid":false,"given":"Yinghuan","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the National Institute of Healthcare Data Science, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-1813","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the National Institute of Healthcare Data Science, Nanjing University, Nanjing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00110"},{"key":"ref2","first-page":"1","article-title":"Segment everything everywhere all at once","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zou"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref4","first-page":"1","article-title":"SLiMe: Segment like me","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Khani"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72995-9_7"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00322"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72120-5_16"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103547"},{"key":"ref10","article-title":"Customized segment anything model for medical image segmentation","author":"Zhang","year":"2023","journal-title":"arXiv:2304.13785"},{"key":"ref11","article-title":"Beyond adapting SAM: Towards end-to-end ultrasound image segmentation via auto prompting","author":"Lin","year":"2023","journal-title":"arXiv:2309.06824"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00337"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103310"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72390-2_67"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101832"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02286"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102880"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3233584"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref21","first-page":"1","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00322"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01959"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI56570.2024.10635501"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01171"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13071590"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107840"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref31","article-title":"SAM 2: Segment anything in images and videos","author":"Ravi","year":"2024","journal-title":"arXiv:2408.00714"},{"key":"ref32","article-title":"SAMM (segment any medical model): A 3D slicer integration to SAM","author":"Liu","year":"2023","journal-title":"arXiv:2304.05622"},{"key":"ref33","article-title":"SAM.MD: Zero-shot medical image segmentation capabilities of the segment anything model","author":"Wald","year":"2023","journal-title":"Medical Imaging With Deep Learning, Short Paper Track"},{"key":"ref34","article-title":"Medical SAM 2: Segment medical images as video via segment anything model 2","author":"Zhu","year":"2024","journal-title":"arXiv:2408.00874"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01960"},{"key":"ref36","article-title":"STU-Net: Scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training","author":"Huang","year":"2023","journal-title":"arXiv:2304.06716"},{"key":"ref37","article-title":"SAM-Med2D","author":"Cheng","year":"2023","journal-title":"arXiv:2308.16184"},{"key":"ref38","article-title":"SAM-Med3D: Towards general-purpose segmentation models for volumetric medical images","author":"Wang","year":"2023","journal-title":"arXiv:2310.15161"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103061"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103324"},{"key":"ref42","first-page":"1","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hu"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.7303\/SYN3193805"},{"key":"ref45","volume-title":"The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results","author":"Everingham","year":"2012"},{"key":"ref46","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2806309"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3100536"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117"},{"key":"ref52","article-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge","author":"Bakas","year":"2018","journal-title":"arXiv:1811.02629"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.10.008"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3162043"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102517"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16452-1_14"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3338269"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11218268\/10847777.pdf?arnumber=10847777","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T17:35:37Z","timestamp":1761759337000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10847777\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":60,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3532084","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]}}}