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Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Due to the complex structure of liver tumors and the low contrast with normal tissues make it still a challenging task to accurately segment liver tumors from CT images. To address these problems, we propose an end-to-end segmentation method for liver tumors.

Methods

The method uses a cascade structure to improve the network’s extraction of information. First, the Side-output Feature Fusion Attention block is used to fuse features at different levels and combine with attention mechanism to focus on important information. Then, the Atrous Spatial Pyramid Pooling Attention block is used to extract multi-scale semantic features. Finally, the Multi-scale Prediction Fusion block is used to fully fused the features captured at each layer of the network.

Result

To verify the performance of the proposed model and the effectiveness of each module, we evaluate it on LiTS and 3DIRCADb datasets and obtained Dice per Case of 0.665 and 0.719, respectively, and Dice Global of 0.812 and 0.784, respectively.

Conclusion

The proposed method is compared with the basic model 3D U-Net, as well as some mainstream methods based on U-Net variants, and our method achieves better performance on the liver tumor segmentation task and is superior to most segmentation algorithms.

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Acknowledgements

This work was partially supported by the Science and Technology Foundation of Guizhou Province (No. ZK[2022]119), the National Science Foundation of China (NSFC, No.61662009) and the Guizhou Province Graduate Research Fund (YJSCXJH[2020]055.

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Author notes

  1. Huaiyan Shen, Yaya Tan and Yucheng Shi have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China

    Yun Wu

  2. College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

    Yun Wu, Huaiyan Shen, Yaya Tan & Yucheng Shi

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  1. Yun Wu
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  2. Huaiyan Shen
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  3. Yaya Tan
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Correspondence to Huaiyan Shen.

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Wu, Y., Shen, H., Tan, Y. et al. Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net. Int J CARS 17, 1915–1922 (2022). https://doi.org/10.1007/s11548-022-02653-9

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  • DOI: https://doi.org/10.1007/s11548-022-02653-9

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