GEU-Net: Rethinking the information transmission in the skip connection of U-Net architecture

Shiqiang Ma, Xuejian Li, Zehua Zhang, Jijun Tang, Fei Guo

Abstract: With the wide application of deep learning technology in medical image processing, the performance of medical image segmentation has been improved in a breakthrough. U-Net architecture has excellent performance in medical image segmentation tasks. In order to solve the problem of image signal loss caused by the autoencoder structure, U-Net has added skip connections to its network to transfer the low-level features of the encoder path to the decoder path. Although this method can roughly solve the problem of image information loss, while it introduces a new problem, that is, the simple feature fusion method causes the high-level semantic information to be diluted. In order to solve the problem that the simple fusion of low-level edge information and high-level semantic information creates the semantic gap and dilutes high-level semantic information, we propose a novel U-shaped architecture, namely GEU-Net. GEU-Net utilizes ensemble learning methods to obtain better segmentation performance with a small computational cost. In addition, We propose a multi-scale group convolution block namely Group Residual (GR) module to reduce the semantic gap between encoder and decoder. We have evaluated our model on the BraTS 2020 Challenge, and have achieved competitive segmentation results.