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

TL;DR

This paper introduces SAMBA, a novel brain tumor segmentation method tailored for African populations using the Segment Anything Model with fine-tuning and ensemble strategies, achieving high accuracy despite low-quality scans.

Contribution

It presents a fine-tuned SAM approach with voting networks for improved glioma segmentation in resource-limited settings, specifically addressing African datasets.

Findings

SAM achieved a Dice coefficient of 86.6 for binary segmentation.

The method demonstrated robustness across multi-modal data.

The approach shows promise for clinical application in resource-limited environments.

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.