  Brain tumor segmentation is a fundamental step in assessing a patient's
cancer progression. However, manual segmentation demands significant expert
time to identify tumors in 3D multimodal brain MRI scans accurately. This
reliance on manual segmentation makes the process prone to intra- and
inter-observer variability. This work proposes a brain tumor segmentation
method as part of the BraTS-GoAT challenge. The task is to segment tumors in
brain MRI scans automatically from various populations, such as adults,
pediatrics, and underserved sub-Saharan Africa. We employ a recent CNN
architecture for medical image segmentation, namely MedNeXt, as our baseline,
and we implement extensive model ensembling and postprocessing for inference.
Our experiments show that our method performs well on the unseen validation set
with an average DSC of 85.54% and HD95 of 27.88. The code is available on
https://github.com/BioMedIA-MBZUAI/BraTS2024_BioMedIAMBZ.
