Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in
  Sub-Sharan African Populations
Mohannad Barakat Noha Magdy Jjuuko George William Ethel Phiri Raymond Confidence Dong Zhang Udunna C Anazodo
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.