Automated brain tumor segmentation in multi-parametric MRI remains a critical yet underserved challenge in resource-constrained clinical settings, where deep 3D networks requiring high-end GPUs are not viable. This is particularly acute across sub-Saharan Africa (SSA), where low-field scanners, heterogeneous patient demographics, and severe data scarcity compound the difficulty of applying standard deep learning pipelines. We present MMRINet, a lightweight segmentation architecture purpose-built for these constraints. At its core, MMRINet replaces quadratic-complexity self-attention with linear-complexity Mamba state-space models, enabling efficient long-range volumetric context modeling without the computational overhead of Transformer-based approaches. We combine two lightweight refinement components:Dual-Path Feature Refinement (DPFR), which extracts complementary detail and contextual representations to improve feature diversity under limited data, and Progressive Feature Aggregation (PFA), which hierarchically fuses multi-scale decoder outputs for sharper segmentation boundaries. Evaluated on the BraTS-Lighthouse SSA 2025 challenge dataset, comprising 3D MRI scans from Nigerian clinical sites, MMRINet achieves an average Dice score of 0.752 and an average HD95 of 12.23 mm with only ~2.5M parameters, outperforming all evaluated baselines, including UNETR, Swin-UNETR, SegMamba, and SegResNet3D. These results indicate that strong validation-set segmentation performance can be achieved with substantially reduced computation, offering a practical step toward AI-assisted neuro-oncology in low-resource clinical environments. Our GitHub repository can be accessed here: BioMedIA-MBZUAI/MMRINet.

翻译：在多参数MRI中的自动脑肿瘤分割仍然是资源受限临床环境中一个关键但服务不足的挑战，因为需要高端GPU的深度3D网络在此场景下不可行。这一问题在撒哈拉以南非洲（SSA）地区尤为严峻，低场强扫描仪、异质性患者人口统计特征以及严重的数据稀缺性共同加剧了应用标准深度学习管线的难度。我们提出MMRINet，一种专为这些约束条件设计的轻量级分割架构。其核心在于，MMRINet用线性复杂度的Mamba状态空间模型替代了二次复杂度的自注意力机制，从而在不引入基于Transformer方法计算开销的情况下，实现高效的长程体素上下文建模。我们结合了两个轻量级精化组件：双路径特征精化（DPFR），该模块提取互补的细节与上下文表示，以增强有限数据下的特征多样性；以及渐进式特征聚合（PFA），该模块分层融合多尺度解码器输出，以获得更清晰的分割边界。在BraTS-Lighthouse SSA 2025挑战赛数据集（包含来自尼日利亚临床站点的3D MRI扫描）上，MMRINet仅使用约250万参数便实现了平均Dice得分0.752和平均HD95为12.23毫米，超越了所有评估的基线方法，包括UNETR、Swin-UNETR、SegMamba和SegResNet3D。这些结果表明，在显著降低计算量的情况下，仍可实现强大的验证集分割性能，为低资源临床环境中AI辅助神经肿瘤学迈出了切实可行的一步。我们的GitHub仓库可访问：BioMedIA-MBZUAI/MMRINet。