Enhancing Encoder with Attention Gate for Multimodal Brain Tumor Segmentation

Yi Li, Zhirui Fang, Di Li, Xin Xie, Yanqing Guo

Abstract: Magnetic Resonance Imaging (MRI) is widely applied to diagnose malignant brain tumors like glioblastoma (GBM). Recent deep network based brain tumor segmentation algorithms have facilitated automatic and accurate segmentation on MRI data, benefiting the clinical diagnosis with efficiency. However, existing methods most work on certain datasets but suffer from performance degradation when tested on unseen out-of-sample datasets. In this paper, we integrate the encoder-decoder network structure with attention gate and Variational Autoencoders (VAE) to achieve promising segmentation results across different situations. Considering there are four modalities in each brain MRI sample, an encoder based on 3D convolution is employed to capture the local correlation among both spatial and modal neighbors. Then the extracted volumetric feature maps are fed into a decoder, finally generating the segmentation results with attention gate module. To facilitate better segmentation, we further adopt VAE as an auxiliary decoder to improve the performance of the encoder.