Abstract
Gliomas are the most prevalent type of primary brain tumors, and their accurate segmentation from MRI is critical for diagnosis, treatment planning, and longitudinal monitoring. However, the scarcity of high-quality annotated imaging data in Sub-Saharan Africa (SSA) poses a significant challenge for deploying advanced segmentation models in clinical workflows. This study introduces a robust and computationally efficient deep learning framework tailored for resource-constrained settings. We leveraged a 3D Attention UNet architecture augmented with residual blocks and enhanced through transfer learning from pre-trained weights on the BraTS 2021 dataset. Our model was evaluated on 95 MRI cases from the BraTS-Africa dataset, a benchmark for glioma segmentation in SSA MRI data. Despite the limited data quality and quantity, our approach achieved Dice scores of 0.76 for the Enhancing Tumor (ET), 0.80 for Necrotic and Non-Enhancing Tumor Core (NETC), and 0.85 for Surrounding Non-Functional Hemisphere (SNFH). These results demonstrate the generalizability of the proposed model and its potential to support clinical decision making in low-resource settings. The compact architecture, approximately 90 MB, and sub-minute per-volume inference time on consumer-grade hardware further underscore its practicality for deployment in SSA health systems. This work contributes toward closing the gap in equitable AI for global health by empowering underserved regions with high-performing and accessible medical imaging solutions.
Abstract (translated)
胶质瘤是最常见的原发性脑肿瘤类型，从MRI图像中对其进行准确分割对于诊断、治疗规划和纵向监测至关重要。然而，在撒哈拉以南非洲（SSA）地区高质量注释成像数据的稀缺给部署高级分割模型带来了重大挑战。本研究介绍了一种专为资源受限环境设计的强大且计算效率高的深度学习框架。我们采用了3D注意力UNet架构，并通过添加残差块进行了增强，同时利用在BraTS 2021数据集上预训练的权重进行迁移学习。我们的模型在包含95例MRI案例的BraTS-Africa数据集（用于SSA MRI数据中胶质瘤分割的标准基准）上进行了评估。尽管面临数据质量和数量的限制，我们的方法在增强肿瘤(ET)、坏死和非增强肿瘤核心(NETC)以及周围无功能半球(SNFH)上的Dice分数分别达到了0.76、0.80 和 0.85。这些结果展示了所提出模型的泛化能力及其支持低资源环境下临床决策制定的潜力。此外，该模型架构精简（约90MB），在消费级硬件上每体素推理时间不到一分钟，进一步突显了其在SSA卫生系统中的实用部署价值。 这项工作有助于弥合全球健康领域人工智能应用不平等的差距，通过为欠发达地区提供高性能且易于访问的医学影像解决方案。
URL
https://arxiv.org/abs/2509.09469
