{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T19:26:08Z","timestamp":1771788368759,"version":"3.50.1"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031818530","type":"print"},{"value":"9783031818547","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-81854-7_11","type":"book-chapter","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T16:08:42Z","timestamp":1739894922000},"page":"167-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Segment Anything in\u00a0Medical Images with\u00a0nnUNet"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5531-1072","authenticated-orcid":false,"given":"Raphael","family":"Stock","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8124-8435","authenticated-orcid":false,"given":"Yannick","family":"Kirchhoff","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4560-0760","authenticated-orcid":false,"given":"Maximilian R.","family":"Rokuss","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2942-6235","authenticated-orcid":false,"given":"Ashis","family":"Ravindran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-2463","authenticated-orcid":false,"given":"Klaus","family":"Maier-Hein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"issue":"2","key":"11_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"11_CR2","doi-asserted-by":"publisher","unstructured":"Isensee, F., Ulrich, C., Wald, T., Maier-Hein, K.H.: Extending nnU-Net is all you need. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds.) BVM 2023. Informatik aktuell, pp. 12\u201317. Springer, Wiesbaden (2023). https:\/\/doi.org\/10.1007\/978-3-658-41657-7_7","DOI":"10.1007\/978-3-658-41657-7_7"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"11_CR5","unstructured":"Wald, T., et al.: SAM.MD: zero-shot medical image segmentation capabilities of the segment anything model. In: Medical Imaging with Deep Learning, Short Paper Track (2023)"},{"issue":"7","key":"11_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100543","volume":"3","author":"Z Xu","year":"2022","unstructured":"Xu, Z., et al.: Codabench: flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns 3(7), 100543 (2022)","journal-title":"Patterns"},{"key":"11_CR7","unstructured":"Zhang, C., et al.: Faster segment anything: towards lightweight SAM for mobile applications. arXiv preprint arXiv:2306.14289 (2023)"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cai, H., Han, S.: EfficientViT-SAM: accelerated segment anything model without performance loss. In: CVPR Workshop: Efficient Large Vision Models (2024)","DOI":"10.1109\/CVPRW63382.2024.00782"}],"container-title":["Lecture Notes in Computer Science","Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-81854-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T09:23:48Z","timestamp":1757582628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-81854-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031818530","9783031818547"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-81854-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"19 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MedSAM on Laptop","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Medical Image Segmentation Challenge","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seattle, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"medsam2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.codabench.org\/competitions\/1847\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}