Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional Unet architectures and their transformer-integrated variants excel in automated segmentation tasks. However, they lack the ability to harness the intrinsic position and channel features of image. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block(DA-Block) into the traditional U-shaped architecture. Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features, improving the performance of medical image segmentation. By incorporating a DA-Block at the embedding layer and within each skip connection layer, we substantially enhance feature extraction capabilities and improve the efficiency of the encoder-decoder structure. DA-TransUNet demonstrates superior performance in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across multiple datasets. In summary, DA-TransUNet offers a significant advancement in medical image segmentation, providing an effective and powerful alternative to existing techniques. Our architecture stands out for its ability to improve segmentation accuracy, thereby advancing the field of automated medical image diagnostics. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

翻译：准确的医学图像分割对于疾病量化与治疗评估至关重要。尽管传统U-Net架构及其Transformer集成变体在自动化分割任务中表现优异，但它们缺乏利用图像固有位置与通道特征的能力。现有模型也因Transformer的广泛使用而面临参数效率与计算复杂性的问题。为解决上述问题，本研究提出一种新颖的深度医学图像分割框架——DA-TransUNet，旨在将Transformer与双重注意力模块（DA-Block）集成至传统U型架构中。与早期基于Transformer的U-Net模型不同，DA-TransUNet通过Transformer和DA-Block不仅融合全局与局部特征，还整合了图像特定的位置与通道特征，从而提升医学图像分割性能。通过在嵌入层及每个跳跃连接层中嵌入DA-Block，我们显著增强了特征提取能力，并优化了编码器-解码器结构的效率。DA-TransUNet在医学图像分割任务中展现出卓越性能，在多个数据集上始终超越现有先进技术。总之，DA-TransUNet为医学图像分割领域提供了重要进展，成为现有技术的一种有效且强大的替代方案。其架构因提升分割精度的能力而脱颖而出，从而推动了自动化医学图像诊断领域的发展。本模型的代码与参数将在https://github.com/SUN-1024/DA-TransUnet 公开提供。