Brain tumors, particularly glioblastoma, continue to challenge medical diagnostics and treatments globally. This paper explores the application of deep learning to multi-modality magnetic resonance imaging (MRI) data for enhanced brain tumor segmentation precision in the Sub-Saharan Africa patient population. We introduce an ensemble method that comprises eleven unique variations based on three core architectures: UNet3D, ONet3D, SphereNet3D and modified loss functions. The study emphasizes the need for both age- and population-based segmentation models, to fully account for the complexities in the brain. Our findings reveal that the ensemble approach, combining different architectures, outperforms single models, leading to improved evaluation metrics. Specifically, the results exhibit Dice scores of 0.82, 0.82, and 0.87 for enhancing tumor, tumor core, and whole tumor labels respectively. These results underline the potential of tailored deep learning techniques in precisely segmenting brain tumors and lay groundwork for future work to fine-tune models and assess performance across different brain regions.

翻译：脑肿瘤，尤其是胶质母细胞瘤，持续对全球医疗诊断与治疗构成挑战。本文探索将深度学习应用于多模态磁共振成像数据，以提升撒哈拉以南非洲患者群体的脑肿瘤分割精度。我们提出一种集成方法，包含基于三种核心架构（UNet3D、ONet3D、SphereNet3D）及改进损失函数的十一种独特变体。研究强调了构建基于年龄和人群的分割模型的必要性，以全面考量大脑的复杂性。我们的发现表明，结合不同架构的集成方法优于单一模型，从而改进了评估指标。具体而言，针对增强肿瘤、肿瘤核心和全肿瘤标签的结果分别实现了0.82、0.82和0.87的Dice得分。这些结果凸显了定制化深度学习技术在精准分割脑肿瘤方面的潜力，并为未来优化模型及评估不同脑区域性能的研究奠定了基础。