Medical Image Segmentation Based on Multiscale Even Convolution Attention U-Net

‍ ‍In order to improve the performance of U-Net and reduce the additional computational complexity as much as possible， a new multiscale even convolution attention U-Net （MECAU-Net） network is proposed in this paper. This network uses 2×2 even convolution instead of 3×3 convolution for feature extraction at the encoder. And inspired by the idea of the multiscale idea， 4×4 even convolution is used to directly transfer the obtained information to the backbone， so as to obtain more comprehensive image information and reduce additional computational cost. At the same time， the symmetric padding is used to solve the shift problem in the process of extracting information from even convolution kernels. In addition， the convolution block attention module is added to combine the spatial and channel attention modules after the 2×2 even convolution module， which can extract richer information without adding additional computational complexity. Finally， simulation experiments are carried out on two medical image datasets. The experimental results show that our proposed MECAU-Net network greatly improves the segmentation performance with slight additional computational cost. In addition， it achieves better segmentation performance than other comparison networks and reduces the amount of parameters.