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

U-Net Transformer: Self and Cross Attention for Medical Image Segmentation