Sub-region segmentation of brain tumors from multimodal MRI images using 3D U-Net

Abstract

Accurate segmentation of brain tumors from the magnetic resonance image (MRI) is an essential step for radionics analysis as well as finding the tumor extension is so necessary to plan the best treatment to improve the survival rate. Manually extracting sub-regions of the brain tumor from MRI is a tedious process and time-consuming, as the complex brain tumor images require extensive human expertise. In recent years, deep learning models have proved effective in medical image segmentation tasks. In brain tumor segmentation, the 3D multimodal MRI poses some challenges such as computation and memory limitations. This study aims to develop a deep learning model using 3D U-Net for brain tumor segmentation. The segmentation results on BraTS 2020 dataset show that the proposed model achieves promising performance.