PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation

Liver cancer seriously threatens human health. In clinical diagnosis, contrast-enhanced computed tomography (CECT) images provide important supplementary information for accurate liver tumor segmentation. However, most of the existing methods of liver tumor automatic segmentation focus only on single-phase image features. And the existing multi-modal methods have limited segmentation effect due to the redundancy of fusion features. In addition, the spatial misalignment of multi-phase images causes feature interference.

In this paper, we propose a phase attention network (PA-Net) to adequately aggregate multi-phase information of CT images and improve segmentation performance for liver tumors. Specifically, we design a PA module to generate attention weight maps voxel by voxel to efficiently fuse multi-phase CT images features to avoid feature redundancy. In order to solve the problem of feature interference in the multi-phase image segmentation task, we design a new learning strategy and prove its effectiveness experimentally.

We conduct comparative experiments on the in-house clinical dataset and achieve the SOTA segmentation performance on multi-phase methods. In addition, our method has improved the mean dice score by 3.3% compared with the single-phase method based on nnUNet, and our learning strategy has improved the mean dice score by 1.51% compared with the ML strategy.

The experimental results show that our method is superior to the existing multi-phase liver tumor segmentation method, and provides a scheme for dealing with missing modalities in multi-modal tasks. In addition, our proposed learning strategy makes more effective use of arterial phase image information and is proven to be the most effective in liver tumor segmentation tasks using thick-layer CT images. The source code is released on (https://github.com/Houjunfeng203934/PA-Net).