Brain cancer affects millions worldwide, and in nearly every clinical setting, doctors rely on magnetic resonance imaging (MRI) to diagnose and monitor gliomas. However, the current standard for tumor quantification through manual segmentation of multi-parametric MRI is time-consuming, requires expert radiologists, and is often infeasible in under-resourced healthcare systems. This problem is especially pronounced in low-income regions, where MRI scanners are of lower quality and radiology expertise is scarce, leading to incorrect segmentation and quantification. In addition, the number of acquired MRI scans in Africa is typically small. To address these challenges, the BraTS-Lighthouse 2025 Challenge focuses on robust tumor segmentation in sub-Saharan Africa (SSA), where resource constraints and image quality degradation introduce significant shifts. In this study, we present EMedNeXt -- an enhanced brain tumor segmentation framework based on MedNeXt V2 with deep supervision and optimized post-processing pipelines tailored for SSA. EMedNeXt introduces three key contributions: a larger region of interest, an improved nnU-Net v2-based architectural skeleton, and a robust model ensembling system. Evaluated on the hidden validation set, our solution achieved an average LesionWise DSC of 0.897 with an average LesionWise NSD of 0.541 and 0.84 at a tolerance of 0.5 mm and 1.0 mm, respectively.

翻译：脑癌影响着全球数百万人，在几乎所有临床环境中，医生都依赖磁共振成像（MRI）来诊断和监测胶质瘤。然而，当前通过人工分割多参数MRI进行肿瘤定量的标准方法耗时耗力，需要专业放射科医生参与，且在资源匮乏的医疗系统中往往难以实施。这一问题在低收入地区尤为突出，这些地区的MRI扫描仪质量较低，放射学专业知识稀缺，导致分割和定量结果错误。此外，非洲地区获取的MRI扫描数量通常较少。为应对这些挑战，BraTS-Lighthouse 2025挑战赛聚焦于撒哈拉以南非洲（SSA）地区在资源受限和图像质量退化导致显著分布偏移下的鲁棒肿瘤分割。本研究提出EMedNeXt——一种基于MedNeXt V2的增强型脑肿瘤分割框架，采用深度监督机制，并针对SSA地区优化了后处理流程。EMedNeXt包含三项核心改进：扩大的感兴趣区域、基于改进型nnU-Net v2的架构骨架，以及鲁棒的模型集成系统。在隐藏验证集上的评估结果显示，我们的解决方案取得了平均病灶级DSC 0.897的成绩，在0.5 mm和1.0 mm容差下的平均病灶级NSD分别为0.541和0.84。