DPCFN: Dual path cross fusion network for medical image segmentation

Abstract

Thanks to the better performance of U-Net in medical segmentation, many U-Net variants have emerged one after another, but U-Net has the non-negligible drawback that it cannot accurately segment low-level features such as edge regions of images, and in addition this simple skip connection of U-Net itself is still a challenge for global information modeling. To solve the above problems, Channel-wise Cross Fusion Transformer (CCT) and Channel-wise Cross Attention (CCA) are introduced on the basis of U-Net, where CCT is used for cross fusion of U-Net encoders and Transformer, and CCA interacts the fused features with the decoder features to eliminate semantic gaps, naming the network Trans-Net. Another branch network SeU-Net is built to capture details and edge regions, and SE-Attention is added at the skip joints of the network to reinforce important features. The two branches interact through a Cross Residual Feature Block (CRFB). By testing on five datasets, it was experimentally demonstrated that the method proposed in this paper has more accurate segmentation performance.