基于多尺度偶数卷积注意力U-Net的医学图像分割

为了提高U-Net网络性能的同时尽可能减少额外计算量，本文提出了一种新的多尺度偶数卷积注意力U-Net （Multiscale Even Convolution Attention U-Net， MECAU-Net）网络。该网络在编码端采用2×2偶数卷积代替3×3卷积进行特征提取，并借鉴多尺度思想，采用4×4偶数卷积将得到的信息直接传递给主干部分，以获取更全面的图像信息并减少额外计算开销，同时还采用对称填充解决偶数卷积提取信息过程中产生的偏移问题。此外，在2×2偶数卷积模块后加入卷积注意力模块，结合空间和通道注意力，在提取更丰富的信息的同时几乎不增加额外开销。最后，在两个医学图像数据集上进行仿真实验，实验结果表明提出的MECAU-Net网络相对于U-Net在稍微增加计算成本的情况下，分割性能得到了较大的提升，并比其他对比网络取得更好的分割性能的同时还降低了参数量。 

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.