Great progress has been made in automatic medical image segmentation due to powerful deep representation learning. The influence of transformer has led to research into its variants, and large-scale replacement of traditional CNN modules. However, such trend often overlooks the intrinsic feature extraction capabilities of the transformer and potential refinements to both the model and the transformer module through minor adjustments. This study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to introduce the Transformer and dual attention block into the encoder and decoder of the traditional U-shaped architecture. Unlike prior transformer-based solutions, our DA-TransUNet utilizes attention mechanism of transformer and multifaceted feature extraction of DA-Block, which can efficiently combine global, local, and multi-scale features to enhance medical image segmentation. Meanwhile, experimental results show that a dual attention block is added before the Transformer layer to facilitate feature extraction in the U-net structure. Furthermore, incorporating dual attention blocks in skip connections can enhance feature transfer to the decoder, thereby improving image segmentation performance. Experimental results across various benchmark of medical image segmentation reveal that DA-TransUNet significantly outperforms the state-of-the-art methods. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

本研究提出了一种名为DA-TransUNet的新型深度医学图像分割框架，将Transformer和双注意力块引入传统U形架构的编码器和解码器中，通过注意力机制和多方面特征提取来增强医学图像分割，并通过实验结果证明DA-TransUNet在各个医学图像分割基准测试中显著优于现有方法。

DA-TransUNet：利用Transformer U-Net集成空间和通道双重注意力进行医学图像分割