Automatic medical image segmentation has made great progress benefit from the development of deep learning. However, most existing methods are based on convolutional neural networks (CNNs), which fail to build long-range dependencies and global context connections due to the limitation of receptive field in convolution operation. Inspired by the success of Transformer in modeling the long-range contextual information, some researchers have expended considerable efforts in designing the robust variants of Transformer-based U-Net. Moreover, the patch division used in vision transformers usually ignores the pixel-level intrinsic structural features inside each patch. To alleviate these problems, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first attempt to concurrently incorporate the advantages of hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture to enhance the semantic segmentation quality of varying medical images. Unlike many prior Transformer-based solutions, the proposed DS-TransUNet first adopts dual-scale encoder subnetworks based on Swin Transformer to extract the coarse and fine-grained feature representations of different semantic scales. As the core component for our DS-TransUNet, a well-designed Transformer Interactive Fusion (TIF) module is proposed to effectively establish global dependencies between features of different scales through the self-attention mechanism. Furthermore, we also introduce the Swin Transformer block into decoder to further explore the long-range contextual information during the up-sampling process. Extensive experiments across four typical tasks for medical image segmentation demonstrate the effectiveness of DS-TransUNet, and show that our approach significantly outperforms the state-of-the-art methods.
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