Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations

• URL: http://arxiv.org/abs/2312.11775v1
• Date: Tue, 19 Dec 2023 01:10:11 GMT
• Title: Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations
• Authors: Mohannad Barakat, Noha Magdy, Jjuuko George William, Ethel Phiri, Raymond Confidence, Dong Zhang and Udunna C Anazodo
• Abstract summary: Gliomas, the most prevalent primary brain tumors, require precise segmentation for diagnosis and treatment planning. We propose an innovative approach combining the Segment Anything Model (SAM) and a voting network for multi-modal glioma segmentation. Our methodology has the potential to profoundly impact clinical practice in resource-limited settings such as Africa.
• Score: 4.567712492463603
• License: http://creativecommons.org/licenses/by/4.0/
• 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, were limited access to high-quality imaging data hampers algorithm performance. In this study, we propose an innovative 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 intra-tumoral heterogeneity. Although the low quality of scans presents difficulties, our methodology has the potential to profoundly impact clinical practice in resource-limited settings such as Africa, improving treatment decisions and advancing neuro-oncology research. Furthermore, successful application to other brain tumor types and lesions in the future holds promise for a broader transformation in neurological imaging, improving healthcare outcomes across all settings. This study was conducted on the Brain Tumor Segmentation (BraTS) Challenge Africa (BraTS-Africa) dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings, particularly the African population, 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 SAM attaining a Dice coefficient of 86.6 for binary segmentation and 60.4 for multi-class segmentation.

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