{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T03:01:48Z","timestamp":1773284508975,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049773","type":"print"},{"value":"9783032049780","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04978-0_60","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:17:39Z","timestamp":1758212259000},"page":"628-637","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TEGDA: Test-Time Evaluation-Guided Dynamic Adaptation for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yubo","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Jianghao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Wenjun","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"60_CR1","unstructured":"Baid, U., et al.: The RSNA-ASNR-MICCAI BRATS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv:2107.02314 (2021)"},{"issue":"1","key":"60_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 170117 (2017)","journal-title":"Sci. Data"},{"key":"60_CR3","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/978-3-031-72111-3_52","volume-title":"MICCAI 2024","author":"H Basak","year":"2024","unstructured":"Basak, H., Yin, Z.: Quest for clone: test-time domain adaptation for medical image segmentation by searching the closest clone in latent space. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15008, pp. 555\u2013566. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_52"},{"issue":"12","key":"60_CR4","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","volume":"40","author":"VM Campello","year":"2021","unstructured":"Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &MS challenge. IEEE Trans. Med. Imaging 40(12), 3543\u20133554 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Pan, Y., Ye, Y., Lu, M., Xia, Y.: Each test image deserves a specific prompt: continual test-time adaptation for 2D medical image segmentation. In: CVPR, pp. 11184\u201311193 (2024)","DOI":"10.1109\/CVPR52733.2024.01063"},{"key":"60_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D u-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"60_CR7","doi-asserted-by":"crossref","unstructured":"Dong, H., Konz, N., Gu, H., Mazurowski, M.A.: Medical image segmentation with InTEnt: integrated entropy weighting for single image test-time adaptation. In: CVPR Workshops, pp. 5046\u20135055 (2024)","DOI":"10.1109\/CVPRW63382.2024.00511"},{"key":"60_CR8","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML, pp. 1050\u20131059. PMLR (2016)"},{"key":"60_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102136","volume":"72","author":"Y He","year":"2021","unstructured":"He, Y., Carass, A., Zuo, L., Dewey, B.E., Prince, J.L.: Autoencoder based self-supervised test-time adaptation for medical image analysis. Med. Image Anal. 72, 102136 (2021)","journal-title":"Med. Image Anal."},{"key":"60_CR10","unstructured":"Jiang, Y., Nagarajan, V., Baek, C., Kolter, J.Z.: Assessing generalization of SGD via disagreement. arXiv:2106.13799 (2021)"},{"key":"60_CR11","unstructured":"Kazerooni, A.F., et al.: The brain tumor segmentation (BRATS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). arXiv:2305.17033 (2023)"},{"key":"60_CR12","doi-asserted-by":"crossref","unstructured":"Lee, T., Chottananurak, S., Gong, T., Lee, S.J.: AETTA: label-free accuracy estimation for test-time adaptation. In: CVPR, pp. 28643\u201328652 (2024)","DOI":"10.1109\/CVPR52733.2024.02706"},{"key":"60_CR13","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"issue":"10","key":"60_CR14","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR15","unstructured":"Nado, Z., Padhy, S., Sculley, D., D\u2019Amour, A., Lakshminarayanan, B., Snoek, J.: Evaluating prediction-time batch normalization for robustness under covariate shift. arXiv:2006.10963 (2020)"},{"key":"60_CR16","unstructured":"Niu, S., et al.: Efficient test-time model adaptation without forgetting. In: ICML, pp. 16888\u201316905. PMLR (2022)"},{"key":"60_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"60_CR18","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: ICML, pp. 9229\u20139248. PMLR (2020)"},{"key":"60_CR19","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS, pp. 1195\u20131204 (2017)"},{"key":"60_CR20","doi-asserted-by":"crossref","unstructured":"Tomar, D., Vray, G., Bozorgtabar, B., Thiran, J.P.: Tesla: test-time self-learning with automatic adversarial augmentation. In: CVPR, pp. 20341\u201320350 (2023)","DOI":"10.1109\/CVPR52729.2023.01948"},{"key":"60_CR21","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: ICLR (2021)"},{"key":"60_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van Gool, L., Dai, D.: Continual test-time domain adaptation. In: CVPR, pp. 7201\u20137211 (2022)","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"60_CR23","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Dynamically instance-guided adaptation: a backward-free approach for test-time domain adaptive semantic segmentation. In: CVPR, pp. 24090\u201324099 (2023)","DOI":"10.1109\/CVPR52729.2023.02307"},{"key":"60_CR24","doi-asserted-by":"crossref","unstructured":"Wu, J., Gu, R., Lu, T., Zhang, S., Wang, G.: UPL-TTA: uncertainty-aware pseudo label guided fully test time adaptation for fetal brain segmentation. In: IPMI, pp. 237\u2013249 (2023)","DOI":"10.1007\/978-3-031-34048-2_19"},{"issue":"9","key":"60_CR25","doi-asserted-by":"publisher","first-page":"3098","DOI":"10.1109\/TMI.2024.3387415","volume":"43","author":"J Wu","year":"2024","unstructured":"Wu, J., et al.: FPL+: filtered pseudo label-based unsupervised cross-modality adaptation for 3D medical image segmentation. IEEE Trans. Med. Imaging 43(9), 3098\u20133109 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"60_CR26","doi-asserted-by":"publisher","first-page":"3575","DOI":"10.1109\/TMI.2022.3191535","volume":"41","author":"H Yang","year":"2022","unstructured":"Yang, H., et al.: DLTTA: dynamic learning rate for test-time adaptation on cross-domain medical images. IEEE Trans. Med. Imaging 41(12), 3575\u20133586 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103275","volume":"97","author":"B Zheng","year":"2024","unstructured":"Zheng, B., et al.: Dual domain distribution disruption with semantics preservation: unsupervised domain adaptation for medical image segmentation. Med. Image Anal. 97, 103275 (2024)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04978-0_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:04:38Z","timestamp":1758233078000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 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":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}