Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and
  Pediatrics
Authors: name: Sarim Hashmi, name: Juan Lugo, name: Abdelrahman Elsayed, name: Dinesh Saggurthi, name: Mohammed Elseiagy, name: Alikhan Nurkamal, name: Jaskaran Walia, name: Fadillah Adamsyah Maani, name: Mohammad Yaqub

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