Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation

摘要

Tumor segmentation is an important research topic in medical image segmentation. With the fast development of deep learning in computer vision, automated segmentation of brain tumors using deep neural networks becomes increasingly popular. U-Net is the most widely-used network in the applications of automated image segmentation. Many well-performed models are built based on U-Net. In this paper, we devise a model that combines the variational-autoencoder regularuzed 3D U-Net model [10] and the MultiResUNet model [7], The model is trained on the 2020 Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset and predicts on the validation set. Our result shows that the modified 3D MultiResUNet performs better than the previous 3D U-Net.