Magnetic Resonance Imaging Image-Based Segmentation of Brain Tumor Using the Modified Transfer Learning Method

PURPOSE: The goal of this study was to improve overall brain tumor segmentation (BraTS) accuracy. In this study, a form of convolutional neural network called three-dimensional (3D) U-Net was utilized to segment various tumor regions on brain 3D magnetic resonance imaging images using a transfer learning technique. MATERIALS AND METHODS: The dataset used for this study was obtained from the multimodal BraTS challenge. The total number of studies was 2240, obtained from BraTS 2018, BraTS 2019, BraTS 2020, and BraTS 2021 challenges, and each study had five series: T1, contrast-enhanced-T1, Flair, T2, and segmented mask file (seg), all in Neuroimaging Informatics Technology Initiative (NIFTI) format. The proposed method employs a 3D U-Net that was trained separately on each of the four datasets by transferring weights across them. RESULTS: The overall training accuracy, validation accuracy, mean dice coefficient, and mean intersection over union achieved were 99.35%, 98.93%, 0.9875%, and 0.8738%, respectively. CONCLUSION: The proposed method for tumor segmentation outperforms the existing method.