Medical image segmentation remains particularly challenging for complex and
low-contrast anatomical structures. In this paper, we introduce the
U-Transformer network, which combines a U-shaped architecture for image
segmentation with self- and cross-attention from Transformers. U-Transformer
overcomes the inability of U-Nets to model long-range contextual interactions
and spatial dependencies, which are arguably crucial for accurate segmentation
in challenging contexts. To this end, attention mechanisms are incorporated at
two main levels: a self-attention module leverages global interactions between
encoder features, while cross-attention in the skip connections allows a fine
spatial recovery in the U-Net decoder by filtering out non-semantic features.
Experiments on two abdominal CT-image datasets show the large performance gain
brought out by U-Transformer compared to U-Net and local Attention U-Nets. We
also highlight the importance of using both self- and cross-attention, and the
nice interpretability features brought out by U-Transformer.

引入了 U-Transformer 网络，结合了用于图像分割的 U 形模型和 Transformer 的自注意和交叉注意力机制，有效地克服了 U-Net 难以对长程上下文交互和空间依赖性建模的问题，在两个腹部 CT 图像数据集上表现出明显的优势。