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Accurate visualization of liver tumors and their surrounding blood vessels isessential for noninvasive diagnosis and prognosis prediction of tumors. Inmedical image segmentation, there is still a lack of in-depth research on thesimultaneous segmentation of liver tumors and peritumoral blood vessels. Tothis end, we collect the first liver tumor, and vessel segmentation benchmarkdatasets containing 52 portal vein phase computed tomography images with liver,liver tumor, and vessel annotations. In this case, we propose a 3D U-shapedCross-Attention Network (UCA-Net) that utilizes a tailored cross-attentionmechanism instead of the traditional skip connection to effectively model theencoder and decoder feature. Specifically, the UCA-Net uses a channel-wisecross-attention module to reduce the semantic gap between encoder and decoderand a slice-wise cross-attention module to enhance the contextual semanticlearning ability among distinct slices. Experimental results show that theproposed UCA-Net can accurately segment 3D medical images and achievestate-of-the-art performance on the liver tumor and intrahepatic vesselsegmentation task.

Towards Simultaneous Segmentation of Liver Tumors and Intrahepatic Vessels via Cross-attention Mechanism