Abstract
Automatic liver tumor segmentation plays a key role in radiation therapy of hepatocellular carcinoma. In this paper, we propose a novel densely connected U-Net model with criss-cross attention (CC-DenseUNet) to segment liver tumors in computed tomography (CT) images. The dense interconnections in CC-DenseUNet ensure the maximum information flow between encoder layers when extracting intra-slice features of liver tumors. Moreover, the criss-cross attention is used in CC-DenseUNet to efficiently capture only the necessary and meaningful non-local contextual information of CT images containing liver tumors. We evaluated the proposed CC-DenseUNet on the LiTS dataset and the 3DIRCADb dataset. Experimental results show that the proposed method reaches the state-of-the-art performance for liver tumor segmentation. We further experimentally demonstrate the robustness of the proposed method on a clinical dataset comprising 20 CT volumes.
Original language | English |
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Pages (from-to) | 3399-3410 |
Number of pages | 12 |
Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 20 |
Issue number | 6 |
DOIs | |
Publication status | Published - 1 Nov 2023 |
Keywords
- CT images
- Dense interconnection
- U-Net
- criss-cross attention
- liver tumor segmentation