{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:19:35Z","timestamp":1775693975650,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032163646","type":"print"},{"value":"9783032163653","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-16365-3_47","type":"book-chapter","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:36:12Z","timestamp":1775691372000},"page":"524-537","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Brain Tumor Segmentation Generalizability via Pseudo-Labeling and Ratio-Adaptive Postprocessing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7173-0939","authenticated-orcid":false,"given":"To-Liang","family":"Hsu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dang Khoa","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5398-9977","authenticated-orcid":false,"given":"Pai","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6925-9999","authenticated-orcid":false,"given":"Ching-Ting","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2239-8174","authenticated-orcid":false,"given":"Wei-Chun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"47_CR1","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1038\/s42256-023-00652-2","volume":"5","author":"A Karargyris","year":"2023","unstructured":"Karargyris, A., et al.: Federated benchmarking of medical artificial intelligence with MedPerf. Nat. Mach. Intell. 5, 799\u2013810 (2023). https:\/\/doi.org\/10.1038\/s42256-023-00652-2","journal-title":"Nat. Mach. Intell."},{"key":"47_CR2","unstructured":"Baid, U., Rane, S.M., Talbar, S.N., et al.: The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv preprint arXiv:2107.02314 (2021)"},{"key":"47_CR3","doi-asserted-by":"crossref","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","DOI":"10.1109\/TMI.2014.2377694"},{"key":"47_CR4","doi-asserted-by":"crossref","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017). https:\/\/doi.org\/10.1038\/sdata.2017.117","DOI":"10.1038\/sdata.2017.117"},{"key":"47_CR5","unstructured":"Adewole, M., Rudie, J.D., Gbadamosi, A., et al.: The brain tumor segmentation (BraTS) challenge 2023: glioma segmentation in sub-Saharan Africa patient population (BraTS-Africa). arXiv preprint arXiv:2305.19369 (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.19369"},{"key":"47_CR6","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Archive (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"key":"47_CR7","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF"},{"key":"47_CR8","unstructured":"LaBella, D., et al.: The ASNR-MICCAI brain tumor segmentation (BraTS) challenge 2023: intracranial meningioma. arXiv preprint arXiv:2305.07642 (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.07642"},{"key":"47_CR9","unstructured":"Moawad, A.W., et al.: The brain tumor segmentation (BraTS-METS) challenge 2023: brain metastasis segmentation on pre-treatment MRI. arXiv preprint arXiv:2306.00838 (2023). https:\/\/doi.org\/10.48550\/arXiv.2306.00838"},{"key":"47_CR10","unstructured":"Kazerooni, A.F., et al.: The brain tumor segmentation (BraTS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). arXiv preprint arXiv:2305.17033 (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.17033"},{"key":"47_CR11","unstructured":"Kazerooni, A.F., et al.: The brain tumor segmentation in pediatrics (BraTS-PEDs) challenge: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). arXiv preprint arXiv:2404.15009 (2024). https:\/\/doi.org\/10.48550\/arXiv.2404.15009"},{"key":"47_CR12","unstructured":"Li, W., Yuille, A., Zhou, Z.: How well do supervised 3D models transfer to medical imaging tasks? arXiv preprint arXiv:2501.11253 (2025). https:\/\/doi.org\/10.48550\/arXiv.2501.11253"},{"key":"47_CR13","doi-asserted-by":"publisher","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18, 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","DOI":"10.1038\/s41592-020-01008-z"},{"key":"47_CR14","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.) Bildverarbeitung f\u00fcr die Medizin 2023, Informatik aktuell, pp. 33\u201338. Springer Vieweg, Wiesbaden (2023). https:\/\/doi.org\/10.1007\/978-3-658-41657-7_7","DOI":"10.1007\/978-3-658-41657-7_7"},{"key":"47_CR15","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net revisited: A call for rigorous validation in 3D medical image segmentation. In: Proceeding MICCAI, pp. 488\u2013498. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Proceeding MICCAI, pp. 605\u2013613. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"47_CR17","doi-asserted-by":"crossref","unstructured":"Assefa, M., Naseer, M., Ganapathi, I.I., Ali, S.S., Seghier, M.L., Werghi, N.: DyCON: dynamic uncertainty-aware consistency and contrastive learning for semi-supervised medical image segmentation. In: Proc. CVPR, pp. 30850\u201330860 (2025)","DOI":"10.1109\/CVPR52734.2025.02873"},{"key":"47_CR18","unstructured":"Ma, J., Li, F., Wang, B.: U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024). https:\/\/doi.org\/10.48550\/arXiv.2401.04722"},{"key":"47_CR19","doi-asserted-by":"publisher","unstructured":"Tampu, I.E., Haj-Hosseini, N., Eklund, A.: Does anatomical contextual information improve 3D U-Net-based brain tumor segmentation? Diagnostics11(7), 1159 (2021). https:\/\/doi.org\/10.3390\/diagnostics11071159","DOI":"10.3390\/diagnostics11071159"},{"issue":"8","key":"47_CR20","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1007\/s11548-024-03165-4","volume":"19","author":"TS Mathai","year":"2024","unstructured":"Mathai, T.S., Liu, B., Summers, R.M.: Segmentation of mediastinal lymph nodes in CT with anatomical priors. Int. J. Comput. Assist. Radiol. Surg. 19(8), 1537\u20131544 (2024). https:\/\/doi.org\/10.1007\/s11548-024-03165-4","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"47_CR21","doi-asserted-by":"publisher","unstructured":"Huang, H., et al.: A deep multi-task learning framework for brain tumor segmentation. Front. Oncol. 11, 690244 (2021). https:\/\/doi.org\/10.3389\/fonc.2021.690244","DOI":"10.3389\/fonc.2021.690244"},{"key":"47_CR22","doi-asserted-by":"publisher","unstructured":"Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B., Reuter, M.: FastSurfer \u2013 A fast and accurate deep learning-based neuroimaging pipeline. NeuroImage 219, 117012 (2020). https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117012","DOI":"10.1016\/j.neuroimage.2020.117012"},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Loss Odyssey in Medical Image Segmentation. Med. Image Anal. 71, 102035 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.102035","DOI":"10.1016\/j.media.2021.102035"},{"key":"47_CR24","doi-asserted-by":"publisher","unstructured":"Zeineldin, R.A., Karar, M.E., Burgert, O., Mathis-Ullrich, F.: Multimodal CNN networks for brain tumor segmentation in MRI: a BraTS 2022 challenge solution. In: Bakas, S., Reyes, M., Rieke, N., Menze, B.H., Baust, M. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022, LNCS, vol. 13769, pp. 140\u2013151. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-33842-7_11","DOI":"10.1007\/978-3-031-33842-7_11"},{"key":"47_CR25","unstructured":"Ferreira, A., et al.: How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced synthetic data augmentation and model ensemble for brain tumour segmentation. arXiv preprint arXiv:2402.17317 (2024). https:\/\/arxiv.org\/abs\/2402.17317"},{"key":"47_CR26","doi-asserted-by":"publisher","unstructured":"Capell\u00e1n-Mart\u00edn, D., et al.: Model ensemble for brain tumor segmentation in magnetic resonance imaging. In: Brain Tumor Segmentation, and Cross-Modality Domain Adaptation for Medical Image Segmentation: MICCAI Challenges, BraTS 2023 and CrossMoDA 2023, LNCS, vol. 14465, pp. 221\u2013232. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-76163-8_20","DOI":"10.1007\/978-3-031-76163-8_20"}],"container-title":["Lecture Notes in Computer Science","Segmentation, Classification, and Synthesis for Brain Tumors and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16365-3_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:36:13Z","timestamp":1775691373000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16365-3_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032163646","9783032163653"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16365-3_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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"}}]}}