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Densely Connected U-Net With Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images | IEEE Journals & Magazine | IEEE Xplore

Densely Connected U-Net With Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images


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...Show More

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.
Page(s): 3399 - 3410
Date of Publication: 15 August 2022

ISSN Information:

PubMed ID: 35984790

Funding Agency:


References

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