Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-02-003 / 352-02-003 ISMRM Abstract

Domain adaptation and Model Compression for Glioma Segmentation in Sub-Saharan African MRI

Primary: Neuro - Tumors
Secondary: Physics & Engineering
352-02-003 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
352-02-003 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
Keywords: Africa Domain adaptation Low-Resource Settings Automated segmentation Light weight
Accepted
Willem Boonzaier1,2, Farhana Moosa 2, Udunna Anazodo3,4,5,6
1Medical Physics, University of the Free State, Bloemfontein, South Africa
2SPRINT AI Training for African Medical Imaging Knowledge Translation (SPARK) Program, Nigeria
3McGill University, Montreal, Canada
4Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
5Montreal Neurological Institute, Montreal, Canada
6Consortium for Advancement of MRI Education and Research in Africa, Canada
Presenting Author: Farhana Moosa

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References

1. 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:2305.19369 [eess.IV] (2023)
2. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific Data 4, 170117 (2017), https://doi.org/10.1038/sdata.2017.117 [doi]
3. Bonato, B., Nanni, L., Bertoldo, A.: Advancing precision: A comprehensive review of mri segmentation datasets from brats challenges (2012–2025). Sensors (Basel, Switzerland) 25(6), 1838 (2025)
4. Celaya, A. et al. "MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework," arXiv preprint arXiv:2407.21343
5. Celaya, A. et al., "PocketNet: A Smaller Neural Network For Medical Image Analysis," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2022.3224873. [doi]
6. Hashmi, S., Lugo, J., Elsayed, A., Saggurthi, D., Elseiagy, M., Nurkamal, A., Walia, J., Maani, F.A., Yaqub, M.: Optimizing brain tumor segmentation with mednext: Brats 2024 ssa and pediatrics. arXiv preprint arXiv:2411.15872 (2024)
7. 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. Nature methods 18(2), 203–211 (2021)
8. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34(10), 1993–2024 (2014), https://doi.org/10.1109/TMI.2014.2377694 [doi]

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http://echo.ismrm.org/p/ISMRM2026/352-02-003