Three-Dimensional Network With Squeeze and Excitation for Accurate Multi-Region Brain Tumor Segmentation

Abstract

Brain tumors involve abnormal cell growth within or adjacent to brain tissues, necessitating precise segmentation for effective clinical decision-making. Traditional models often face challenges in accurately delineating tumor regions, and building robust segmentation models for high-resolution MRI data requires substantial computational power. This study presents a three-dimensional U-Net architecture with Squeeze and Excitation (SE) modules, called SE-3D Brain Net, to enhance multi-region brain tumor segmentation. The model leverages SE modules to recalibrate channel-wise feature significance, improving segmentation accuracy across tumor subregions. Extensive experiments on datasets such as BraTS 2018 and BraTS 2020 demonstrate that the model outperforms traditional U-Net models and various advanced methods, achieving average Dice scores of 0.86 for enhancing tumor, 0.84 for tumor core, and 0.86 for whole tumor segmentation. An ablation study further revealed the model's sensitivity to hyperparameters, identifying optimal settings for batch size, learning rate, and dropout rate. This study demonstrates the effectiveness of deep learning in accurately identifying brain tumors, emphasizing its potential to improve medical image analysis and patient outcomes significantly.