Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Saharan African Populations
Barakat M, Magdy N, William JG, Phiri E, Confidence R, Zhang D, Anazodo UC (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 14669 LNCS
Pages Range: 200-210
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Vancouver, BC, CAN
ISBN: 9783031761621
DOI: 10.1007/978-3-031-76163-8_18
Abstract
Gliomas, the most prevalent primary brain tumors, require precise segmentation for diagnosis and treatment planning. However, this task poses significant challenges, particularly in the African population, where limited access to high-quality imaging data hampers algorithm performance. In this study, we propose a new approach combining the Segment Anything Model (SAM) and a voting network for multi-modal glioma segmentation. By fine-tuning SAM with bounding box-guided prompts (SAMBA), we adapt the model to the complexities of African datasets. Our ensemble strategy, utilizing multiple modalities and views, produces a robust consensus segmentation, addressing the intratumoral heterogeneity. This study was conducted on the Brain Tumor Segmentation (BraTS) Africa (BraTS-Africa) dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings and facilitating the development of effective and more generalizable segmentation algorithms. To illustrate our approach’s potential, our experiments on the BraTS-Africa dataset yielded compelling results, with SAMBA attaining a Dice coefficient of 86.6% for binary segmentation and 60.4% for multi-class segmentation. Although the low quality of the scans currently presents difficulties, SAMBA has the potential to facilitate more generalizable segmentations for real world clinical problems with future applications to other types of brain lesions.
Involved external institutions
Makerere University
Uganda (UG)
Malawi University of Science and Technology
Malawi (MW)
University of British Columbia
Canada (CA)
Uganda (UG)
Malawi University of Science and Technology
Malawi (MW)
University of British Columbia
Canada (CA)
How to cite
APA:
Barakat, M., Magdy, N., William, J.G., Phiri, E., Confidence, R., Zhang, D., & Anazodo, U.C. (2024). Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Saharan African Populations. In Ujjwal Baid, Sylwia Malec, Spyridon Bakas, Reuben Dorent, Monika Pytlarz, Alessandro Crimi, Ruisheng Su, Navodini Wijethilake (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 200-210). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.
MLA:
Barakat, Mohannad, et al. "Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Saharan African Populations." Proceedings of the Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2023, Vancouver, BC, CAN Ed. Ujjwal Baid, Sylwia Malec, Spyridon Bakas, Reuben Dorent, Monika Pytlarz, Alessandro Crimi, Ruisheng Su, Navodini Wijethilake, Springer Science and Business Media Deutschland GmbH, 2024. 200-210.
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