Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, has proven highly successful for various medical image segmentation tasks. However, U-Net's convolution-based operations inherently limit its ability to model long-range dependencies effectively. To address these limitations, researchers have turned to Transformers, renowned for their global self-attention mechanisms, as alternative architectures. One popular network is our previous TransUNet, which leverages Transformers' self-attention to complement U-Net's localized information with the global context. In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design. We introduce two key components: 1) A Transformer encoder that tokenizes image patches from a convolution neural network (CNN) feature map, enabling the extraction of global contexts, and 2) A Transformer decoder that adaptively refines candidate regions by utilizing cross-attention between candidate proposals and U-Net features. Our investigations reveal that different medical tasks benefit from distinct architectural designs. The Transformer encoder excels in multi-organ segmentation, where the relationship among organs is crucial. On the other hand, the Transformer decoder proves more beneficial for dealing with small and challenging segmented targets such as tumor segmentation. Extensive experiments showcase the significant potential of integrating a Transformer-based encoder and decoder into the u-shaped medical image segmentation architecture. TransUNet outperforms competitors in various medical applications.

通过在2D TransUNet体系结构的基础上建立在最先进nnU-Net体系结构的基础上，充分探索Transformers在编码器和解码器设计中的潜力，我们引入了两个关键组件：1）一个从卷积神经网络（CNN）特征图中令图像块标记化的Transformer编码器，从而实现全局上下文的提取；2）一个自适应地利用候选区域和U-Net特征之间的交叉注意力进行候选区域的精炼的Transformer解码器。我们的研究发现，不同的医学任务受益于不同的体系结构设计。Transformer编码器在多器官分割中表现出色，其中器官之间的关系至关重要。另一方面，Transformer解码器在处理小而具有挑战性的分割目标（如肿瘤分割）方面更有益处。大量实验证明了将基于Transformer的编码器和解码器集成到U型医学图像分割体系结构中的巨大潜力。TransUNet在各种医学应用中超越竞争对手。

3D TransUNet：通过视觉Transformer提升医学图像分割