EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-saharan Africa Using MedNeXt V2 with Deep Supervision
Jaheen, Ahmed Elsayed, Abdelrahman Kim, Damir Tikhonov, Daniil Scatolin, Matheus Banerjee, Mohor Ji, Qiankun Salem, Mostafa Wang, Hu Hashmi, Sarim
Jaheen, Ahmed
Elsayed, Abdelrahman
Kim, Damir
Tikhonov, Daniil
Scatolin, Matheus
Banerjee, Mohor
Ji, Qiankun
Salem, Mostafa
Wang, Hu
Hashmi, Sarim
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Department
Computer Vision
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Conference proceeding
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Abstract
Brain tumors, particularly gliomas, pose a significant global health burden, with magnetic resonance imaging (MRI) serving as the primary tool for diagnosis and disease monitoring. 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 0.5mm and 1.0mm, respectively. Our GitHub repository can be accessed here: Project Repository.
Citation
A. Jaheen, A. Elsayed, D. Kim, D. Tikhonov, M. Scatolin, M. Banerjee , et al., "EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-saharan Africa Using MedNeXt V2 with Deep Supervision," 2026, pp. 224-236.
Source
Lecture Notes in Computer Science
Conference
International Conference on Medical Image Computing and Computer-Assisted Intervention
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46 Information and Computing Sciences
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Source
International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher
Springer Nature