{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:44:21Z","timestamp":1775695461624,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"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_30","type":"book-chapter","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:45:12Z","timestamp":1775691912000},"page":"328-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reassessing Glioma Segmentation Strategies: nnU-Net as\u00a0a\u00a0Strong Baseline on\u00a0Limited Sub-Saharan MRI Data"],"prefix":"10.1007","author":[{"given":"Uwimana","family":"Lowami","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew Blayama","family":"Stephen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Confidence","family":"Raymond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maruf","family":"Adewole","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8864-035X","authenticated-orcid":false,"given":"Udunna C.","family":"Anazodo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehmet","family":"Kurt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Damilare","family":"Olatunji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernes Lorier","family":"Atabonfack","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"8","key":"30_CR1","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","volume":"23","author":"DN Louis","year":"2021","unstructured":"Louis, D.N., Perry, A., Wesseling, P., Brat, D.J., Cree, I.A., Figarella-Branger, D., et al.: The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 23(8), 1231\u20131251 (2021)","journal-title":"Neuro Oncol."},{"key":"30_CR2","unstructured":"Adewole, M., et\u00a0al.: The brain tumor segmentation (BraTS) challenge 2023: Glioma segmentation in sub-saharan africa patient population (BraTS-Africa). arXiv preprint arXiv:2305.19369 (2023)"},{"issue":"9","key":"30_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.3112","volume":"13","author":"O Uwishema","year":"2023","unstructured":"Uwishema, O., et al.: Epidemiology and etiology of brain cancer in africa: a systematic review. Brain and Behav. 13(9), e3112 (2023)","journal-title":"Brain and Behav."},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Lee, J,H., Wee, C.W.: Treatment of adult gliomas: a current update. Brain & NeuroRehabilitation 15(3), e24 (2022)","DOI":"10.12786\/bn.2022.15.e24"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR, abs\/ arXiv:1505.04597 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"30_CR6","unstructured":"Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. CoRR, abs\/ arXiv:1804.03999 (2018)"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Nawaz, A., Akram, U., Salam, A.A., Ali, A.R., Rehman, A.U., Zeb, J.: Vgg-unet for brain tumor segmentation and ensemble model for survival prediction. In: 2021 International Conference on Robotics and Automation in Industry (ICRAI), pp. 1\u20136 (2021)","DOI":"10.1109\/ICRAI54018.2021.9651367"},{"key":"30_CR8","unstructured":"Cahall, D., Rasool, G., Bouaynaya, N.C., Fathallah-Shaykh, H.M.: Dilated inception u-net (diu-net) for brain tumor segmentation. arXiv preprint arXiv:2108.06772 (2021)"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Zeineldin, R.A., Karar, M.E., Coburger, J., Wirtz, C.R., Burgert, O.: Deepseg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance flair images. arXiv preprint arXiv:2004.12333 (2020)","DOI":"10.1007\/s11548-020-02186-z"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net: self-adapting framework for U-Net-Based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"30_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/978-3-030-72087-2_11","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"F Isensee","year":"2021","unstructured":"Isensee, F., J\u00e4ger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnU-Net for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 118\u2013132. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72087-2_11"},{"key":"30_CR12","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. BraTS 2023 Challenge Submission Paper. Internal document from the BraTS 2023 challenge submission (2023)"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin Transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"30_CR14","doi-asserted-by":"crossref","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. arXiv preprint arXiv:2212.09310 (2022)","DOI":"10.1007\/978-3-031-33842-7_11"},{"key":"30_CR15","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. CoRR, abs\/ arXiv:2010.11929 (2020)"},{"key":"30_CR16","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. LNCS, vol. 12962. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. CoRR, abs\/ arXiv:2201.03545 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Roy, S., et al.: MedNeXt: transformer-driven scaling of ConvNets. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 405\u2013415. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_39","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Xing, Z., Ye, T., Yang, Y., Liu, G., Zhu, L.: Segmamba: long-range sequential modeling mamba for 3d medical image segmentation (2024)","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"30_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1007\/978-3-030-11726-9_40","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"R McKinley","year":"2019","unstructured":"McKinley, R., Meier, R., Wiest, R.: Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 456\u2013465. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_40"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnu-net revisited: A call for rigorous validation in 3d medical image segmentation (2024)","DOI":"10.1007\/978-3-031-72114-4_47"}],"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_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:45:14Z","timestamp":1775691914000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16365-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032163646","9783032163653"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16365-3_30","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"}}]}}