  Accurate visualization of liver tumors and their surrounding blood vessels is
essential for noninvasive diagnosis and prognosis prediction of tumors. In
medical image segmentation, there is still a lack of in-depth research on the
simultaneous segmentation of liver tumors and peritumoral blood vessels. To
this end, we collect the first liver tumor, and vessel segmentation benchmark
datasets containing 52 portal vein phase computed tomography images with liver,
liver tumor, and vessel annotations. In this case, we propose a 3D U-shaped
Cross-Attention Network (UCA-Net) that utilizes a tailored cross-attention
mechanism instead of the traditional skip connection to effectively model the
encoder and decoder feature. Specifically, the UCA-Net uses a channel-wise
cross-attention module to reduce the semantic gap between encoder and decoder
and a slice-wise cross-attention module to enhance the contextual semantic
learning ability among distinct slices. Experimental results show that the
proposed UCA-Net can accurately segment 3D medical images and achieve
state-of-the-art performance on the liver tumor and intrahepatic vessel
segmentation task.
