On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks
This work addresses the need for automated brain tumor segmentation to reduce manual effort and variability in medical imaging, but it is incremental as it builds on existing methods.
The authors tackled brain tumor segmentation across diverse populations using a CNN-based method, achieving an average Dice Similarity Coefficient of 85.54% and Hausdorff Distance 95 of 27.88 on an unseen validation set.
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.