{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:35:29Z","timestamp":1778344529386,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"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_45","type":"book-chapter","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:37:20Z","timestamp":1775691440000},"page":"500-511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with\u00a0Multimodal MRI"],"prefix":"10.1007","author":[{"given":"Gerard","family":"Comas-Quiles","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8139-9647","authenticated-orcid":false,"given":"Carles","family":"Garcia-Cabrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9980-0910","authenticated-orcid":false,"given":"Julia","family":"Dietlmeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4033-9135","authenticated-orcid":false,"given":"Noel E.","family":"O\u2019Connor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8311-1168","authenticated-orcid":false,"given":"Ferran","family":"Marques","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"45_CR1","doi-asserted-by":"publisher","unstructured":"Kim, Y.W., Mansfield, L.T.: Fool me twice: Delayed diagnoses in radiology with emphasis on perpetuated errors. Am. J. Roentgenol., 202(3), 465\u2013470 (2014). https:\/\/doi.org\/10.2214\/AJR.13.11493. PMID: 24555582","DOI":"10.2214\/AJR.13.11493"},{"key":"45_CR2","doi-asserted-by":"publisher","unstructured":"Bruno, M.A., Walker, E.A., Abujudeh, H.H.: Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. In: RadioGraphics. vol. 35, no. 6, pp. 1668\u20131676 (2015). https:\/\/doi.org\/10.1148\/rg.2015150023. PMID: 26466178","DOI":"10.1148\/rg.2015150023"},{"key":"45_CR3","unstructured":"Menze, B.H., Jakab, A., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging (2015)"},{"key":"45_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 (2017)","DOI":"10.1038\/sdata.2017.117"},{"key":"45_CR5","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. MICCAI (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"45_CR6","doi-asserted-by":"crossref","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 (2021)","DOI":"10.1038\/s41592-020-01008-z"},{"key":"45_CR7","unstructured":"Kim, J.W., Khan, A.U., Banerjee, I., et\u00a0al.: Unetr: transformers for 3d medical image segmentation. CVPR (2022)"},{"key":"45_CR8","unstructured":"Chen, J., Lu, Y., Yu, Q., et\u00a0al.: Transunet: transformers make strong encoders for medical image segmentation. In: MICCAI (2021)"},{"key":"45_CR9","doi-asserted-by":"publisher","first-page":"06","DOI":"10.1186\/s40658-025-00767-y","volume":"12","author":"S de Sutter","year":"2025","unstructured":"de Sutter, S., et al.: Interobserver ground-truth variability limits performance of automated glioblastoma segmentation on [f]fet pet. EJNMMI Phys. 12, 06 (2025). https:\/\/doi.org\/10.1186\/s40658-025-00767-y","journal-title":"EJNMMI Phys."},{"key":"45_CR10","doi-asserted-by":"publisher","unstructured":"van der Loo, I., Bucho, T.M.T., Hanley, J.A., Beets-Tan, R.G., Imholz, A.L., Trebeschi, S.: Measurement variability of radiologists when measuring brain tumors. European J. Radiol. 183, 111874 (2025). https:\/\/doi.org\/10.1016\/j.ejrad.2024.111874, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0720048X24005904","DOI":"10.1016\/j.ejrad.2024.111874"},{"key":"45_CR11","unstructured":"Bakas, S., et\u00a0al.: The 2023 brats challenge: multi-institutional analysis of brain tumor segmentation and generalization. arXiv preprint arXiv:2309.07347 (2023)"},{"key":"45_CR12","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"45_CR13","doi-asserted-by":"crossref","unstructured":"Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. (2021)","DOI":"10.1016\/j.media.2020.101952"},{"key":"45_CR14","unstructured":"Zimmerer, D., Kohl, S.A., Petersen, J., Isensee, F., Maier-Hein, K.H.: Context-encoding variational autoencoder for unsupervised anomaly detection. In: MICCAI (2019)"},{"key":"45_CR15","unstructured":"Xi,\u00a0C., Ender, K.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. MedIA (2018)"},{"key":"45_CR16","doi-asserted-by":"crossref","unstructured":"Venkataramanan, S., Peng, K.C., Singh, R.V., Mahalanobis, A.: Attention guided anomaly localization in images (2020). https:\/\/arxiv.org\/abs\/1911.08616","DOI":"10.1007\/978-3-030-58520-4_29"},{"key":"45_CR17","doi-asserted-by":"publisher","unstructured":"Wijanarko, H., Calista, E., Chen, L.F., Chen, Y.S.: Tri-vae: Triplet variational autoencoder for unsupervised anomaly detection in brain tumor MRI. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3930\u20133939 (2024). https:\/\/doi.org\/10.1109\/CVPRW63382.2024.00397","DOI":"10.1109\/CVPRW63382.2024.00397"},{"key":"45_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J., Liu, Y., Cheng, Y., Qi, Y.: Semisam: enhancing semi-supervised medical image segmentation via SAM-assisted consistency regularization (2024). https:\/\/arxiv.org\/abs\/2312.06316","DOI":"10.1109\/BIBM62325.2024.10821951"},{"key":"45_CR19","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/TMI.2014.2377694","volume":"99","author":"B Menze","year":"2014","unstructured":"Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 99, 12 (2014). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"key":"45_CR20","doi-asserted-by":"publisher","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data, 4 (2017). https:\/\/doi.org\/10.1038\/sdata.2017.117","DOI":"10.1038\/sdata.2017.117"},{"key":"45_CR21","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et\u00a0al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Archive (2017)"},{"key":"45_CR22","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et\u00a0al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive (2017)"},{"key":"45_CR23","unstructured":"Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification (2021). https:\/\/arxiv.org\/abs\/2107.02314"},{"key":"45_CR24","unstructured":"LaBella, D., et al.: The ASNR-MICCAI brain tumor segmentation (brats) challenge 2023: intracranial meningioma (2023). https:\/\/arxiv.org\/abs\/2305.07642"},{"key":"45_CR25","unstructured":"Moawad, A.W., et al.: The brain tumor segmentation (brats-mets) challenge 2023: brain metastasis segmentation on pre-treatment MRI (2024). https:\/\/arxiv.org\/abs\/2306.00838"},{"key":"45_CR26","unstructured":"Adewole, M., et al.: The brain tumor segmentation (brats) challenge 2023: glioma segmentation in sub-saharan africa patient population (brats-africa) (2023). https:\/\/arxiv.org\/abs\/2305.19369"},{"key":"45_CR27","unstructured":"Kazerooni, A.F., et al.: The brain tumor segmentation (brats) challenge 2023: Focus on pediatrics (cbtn-connect-dipgr-asnr-miccai brats-peds) (2024). https:\/\/arxiv.org\/abs\/2305.17033"},{"key":"45_CR28","doi-asserted-by":"publisher","unstructured":"Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: VT-ADL: a vision transformer network for image anomaly detection and localization. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), pp. 01\u201306. IEEE (2021). https:\/\/doi.org\/10.1109\/isie45552.2021.9576231","DOI":"10.1109\/isie45552.2021.9576231"},{"key":"45_CR29","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"1","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1, 62\u201366 (1979). https:\/\/doi.org\/10.1109\/TSMC.1979.4310076","journal-title":"IEEE Trans. Syst. Man Cybern."}],"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_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:37:22Z","timestamp":1775691442000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16365-3_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032163646","9783032163653"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16365-3_45","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"}}]}}