On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks

TL;DR

This paper introduces an automatic brain tumor segmentation method using CNNs, specifically MedNeXt, to improve accuracy across diverse populations and reduce manual effort in MRI analysis.

Contribution

It applies a recent CNN architecture with ensembling and postprocessing to enhance tumor segmentation across varied populations, addressing generalization challenges.

Findings

Achieved an average DSC of 85.54% on validation data.

Demonstrated effective segmentation across diverse populations.

Provided publicly available code for reproducibility.

Abstract

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.