{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T23:57:52Z","timestamp":1769039872049,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031811005","type":"print"},{"value":"9783031811012","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\/978-3-031-81101-2_3","type":"book-chapter","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T06:23:11Z","timestamp":1738650191000},"page":"22-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian Uncertainty Estimation Improves nnU-Net Generalization to\u00a0Unseen Sites for\u00a0Stroke Lesion Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9802-4419","authenticated-orcid":false,"given":"Linda","family":"Vorberg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6168-7403","authenticated-orcid":false,"given":"Hendrik","family":"Ditt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1060-3759","authenticated-orcid":false,"given":"Michael","family":"S\u00fchling","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-5284","authenticated-orcid":false,"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1832-7453","authenticated-orcid":false,"given":"Nicolas","family":"Murray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4203-6605","authenticated-orcid":false,"given":"Savvas","family":"Nicolaou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5668-8092","authenticated-orcid":false,"given":"Oliver","family":"Taubmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","first-page":"2456550","DOI":"10.1155\/2022\/2456550","volume":"2022","author":"L Cui","year":"2022","unstructured":"Cui, L., et al.: Deep learning in ischemic stroke imaging analysis: a comprehensive review. Biomed. Res. Int. 2022(1), 2456550 (2022)","journal-title":"Biomed. Res. Int."},{"key":"3_CR2","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"issue":"2","key":"3_CR3","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"},{"issue":"4","key":"3_CR4","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s11548-022-02570-x","volume":"17","author":"SY Lin","year":"2022","unstructured":"Lin, S.Y., et al.: Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography. Int. J. Comput. Assist. Radiol. Surg. 17(4), 661\u2013671 (2022)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"12","key":"3_CR5","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P., Kapur, T.: Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imaging 39(12), 3868\u20133878 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-019-0085-6","volume":"3","author":"O \u00d6man","year":"2019","unstructured":"\u00d6man, O., M\u00e4kel\u00e4, T., Salli, E., Savolainen, S., Kangasniemi, M.: 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. European Radiol. Experimental 3, 1\u201311 (2019)","journal-title":"European Radiol. Experimental"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Ostmeier, S., et al.: Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT. arXiv:2309.03930 (2023)","DOI":"10.1161\/str.54.suppl_1.TMP69"},{"issue":"8","key":"3_CR8","doi-asserted-by":"publisher","first-page":"e93","DOI":"10.1161\/CIR.0000000000001123","volume":"147","author":"CW Tsao","year":"2023","unstructured":"Tsao, C.W., et al.: Heart disease and stroke statistics-2023 update: a report from the American Heart association. Circulation 147(8), e93\u2013e621 (2023)","journal-title":"Circulation"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Vorberg, L., Taubmann, O., Ditt, H., Maier, A.: Segmentation of acute ischemic stroke in native and enhanced CT using uncertainty-aware labels. In: BVM Workshop. pp. 267\u2013272. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-658-44037-4_72","DOI":"10.1007\/978-3-658-44037-4_72"},{"key":"3_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102749","volume":"85","author":"L de Vries","year":"2023","unstructured":"de Vries, L., Emmer, B.J., Majoie, C.B., Marquering, H.A., Gavves, E.: PerfU-net: baseline infarct estimation from CT perfusion source data for acute ischemic stroke. Med. Image Anal. 85, 102749 (2023)","journal-title":"Med. Image Anal."},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Yang, C., Schweidtmann, A., Tao, Q.: Efficient bayesian uncertainty estimation for nnU-Net. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 535\u2013544. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_51","DOI":"10.1007\/978-3-031-16452-1_51"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., Fu, H.: A review of uncertainty estimation and its application in medical imaging. Meta-Radiol., 100003 (2023)","DOI":"10.1016\/j.metrad.2023.100003"}],"container-title":["Lecture Notes in Computer Science","Image Analysis in Stroke Diagnosis and Interventions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-81101-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T06:23:26Z","timestamp":1738650206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-81101-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031811005","9783031811012"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-81101-2_3","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":"5 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SWITCH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stroke Workshop on Imaging and Treatment Challenges","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"switch2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/switchmiccai.github.io\/switch\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}