3D U-Net-Based Brain Tumor Semantic Segmentation Using a Modified Data Generator

Abstract

Brain tumors, particularly gliomas, pose a significant global health challenge, causing numerous fatalities annually. Among gliomas, glioblastoma stands out as a highly aggressive type, often resulting in severe symptoms. Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) data is crucial for effective diagnosis and treatment planning. This study introduces a novel 3D U-Net semantic segmentation model with a modified data generator approach, specifically tailored for the brain tumor segmentation (BraTS) 2020 dataset. The modified data generator is unique in that it performs on-the-fly data augmentation, generating diverse and distinct data samples during training. This approach reduces overfitting and enhances generalization, which is critical for handling the variability of brain tumor presentations. The model was trained end-to-end without weight transfer, optimizing the dice score as the primary evaluation metric. The proposed model achieved dice scores of 82.2%, 90.3%, and 77.8% for tumor core, whole tumor, and enhancing tumor regions, respectively, on the BraTS 2020 validation dataset. The minimal variation from training data underscores the model's robustness and reliability in segmenting different tumor subtypes. The modified data generator approach presents a promising advancement for brain tumor segmentation, with the potential for significant improvements in treatment planning and patient outcomes. This model could support more accurate and robust segmentation in clinical applications by effectively addressing data variability.