Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics

TL;DR

This paper introduces MedNeXt, a machine learning approach for brain tumor segmentation that effectively handles diverse MRI data, demonstrating high accuracy and robustness on BraTS 2024 SSA and Pediatric datasets.

Contribution

The study presents MedNeXt combined with ensembling and postprocessing techniques, improving segmentation reliability across diverse populations and MRI qualities in BraTS 2024 challenge.

Findings

Achieved DSC of 0.896 on SSA dataset

Achieved DSC of 0.830 on Pediatric dataset

Demonstrated robustness to data distribution shifts

Abstract

Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert intervention and is susceptible to human error. Therefore, significant research has been devoted to developing machine learning methods that can accurately segment tumors in 3D multimodal brain MRI scans. Despite their progress, state-of-the-art models are often limited by the data they are trained on, raising concerns about their reliability when applied to diverse populations that may introduce distribution shifts. Such shifts can stem from lower quality MRI technology (e.g., in sub-Saharan Africa) or variations in patient demographics (e.g., children). The BraTS-2024 challenge provides a platform to address these issues. This study presents our methodology for segmenting tumors in the BraTS-2024 SSA and Pediatric Tumors tasks using MedNeXt, comprehensive model ensembling, and thorough postprocessing. Our approach demonstrated strong performance on the unseen validation set, achieving an average Dice Similarity Coefficient (DSC) of 0.896 on the BraTS-2024 SSA dataset and an average DSC of 0.830 on the BraTS Pediatric Tumor dataset. Additionally, our method achieved an average Hausdorff Distance (HD95) of 14.682 on the BraTS-2024 SSA dataset and an average HD95 of 37.508 on the BraTS Pediatric dataset. Our GitHub repository can be accessed here: Project Repository : https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics