HCT-Unet: multi-target medical image segmentation via a hybrid CNN-transformer Unet incorporating multi-axis gated multi-layer perceptron

In recent years, for the purpose of integrating the individual strengths of convolutional neural networks (CNN) and Transformer, a network structure has been built to integrate the two methods in medical image segmentation. But most of the methods only integrate CNN and Transformer at a single level and cannot extract low-level detail features and high-level abstract information simultaneously. Meanwhile, this structure lacks flexibility, unable to dynamically adjust the contributions of different feature maps. To address these limitations, we introduce HCT-Unet, a hybrid CNN-Transformer model specifically designed for multi-organ medical images segmentation. HCT-Unet introduces a tunable hybrid paradigm that differs significantly from conventional hybrid architectures. It deploys powerful CNN to capture short-range information and Transformer to extract long-range information at each stage. Furthermore, we have designed a multi-functional multi-scale fusion bridge, which progressively integrates information from different scales and dynamically modifies attention weights for both local and global features. With the benefits of these two innovative designs, HCT-Unet demonstrates robust discriminative dependency and representation capabilities in multi-target medical image tasks. Experimental results reveal the remarkable performance of our approach in medical image segmentation tasks. Specifically, in multi-organ segmentation tasks, HCT-Unet achieved a Dice similarity coefficient (DSC) of 82.23%. Furthermore, in cardiac segmentation tasks, it reached a DSC of 91%, significantly outperforming previous state-of-the-art networks. The code has been released on Zenodo: https://zenodo.org/doi/10.5281/zenodo.11070837.