Skip to main content
SpringerLink
Log in

A Segmentation Network for CT Image of Hepatocellular Carcinoma Based on Attention Block

  • Conference paper
  • First Online:
Proceedings of 2021 Chinese Intelligent Systems Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 805))

  • 1758 Accesses

Abstract

Hepatocellular carcinoma is one of the most diagnosed cancers in the world today. Accurate image segmentation based on CT image of liver and tumors plays an essential role in the treatment of hepatocellular carcinoma. This paper proposed an improved U-Net segmentation network based on attention mechanism for automatic segmentation of liver and tumor in CT images of the abdomen. The channel attention mechanism used in this paper not only considers the different importance of different channels for segmentation results but also considers the interdependence between various channels. At the same time, in order to prevent feature loss, we add a deep supervision module in the network. The proposed method in this paper is tested on the public dataset LiTS, and the results of the segmentation show that our method has better segmentation performance compared with the existing main methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 449.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, H., et al.: Visualizing liver anatomy, physiology and pharmacology using multiphoton microscopy. J. Biophotonics 10, 46–60 (2017)

    Google Scholar 

  2. Zhu, J.X., et al.: GPC3-targeted and curcumin-loaded phospholipid microbubbles for sono-photodynamic therapy in liver cancer cells. Colloids Surf., B 197, 11358 (2021)

    Article  Google Scholar 

  3. Tomoshige, S., Oost, E., Shimizu, A., Watanabe, H., Nawano, S.: A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images. Med. Image Anal. 18(1), 130–143 (2014)

    Article  Google Scholar 

  4. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  Google Scholar 

  5. Hoogi, A., Beaulieu, C.F., Cunha, G.M., Heba, E., Rubin, D.L.: Adaptive local window for level set segmentation of CT and MRI liver lesions. Med. Image Anal. 37, 46–55 (2017)

    Article  Google Scholar 

  6. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (2015)

    Google Scholar 

  7. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2015)

    Google Scholar 

  8. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Article  Google Scholar 

  9. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H.: Fully convolutional network for liver segmentation and lesions detection. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 77–85. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_9

    Chapter  Google Scholar 

  10. Sun, C., et al.: Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNsg. Artif. Intell. Med. 83, 58–66 (2017)

    Article  Google Scholar 

  11. Delmoral, J.C., Costa, D.C., Borges, D., Tavares, J.M.R.S.: Segmentation of pathological liver tissue with dilated fully convolutional networks: a preliminary study. In: 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), pp. 1–4 (2019)

    Google Scholar 

  12. Pang, S., Du, A., Yu, Z., Orgun, M.A.: Correlation matters: multi-scale fine-grained contextual information extraction for hepatic tumor segmentation. In: Lauw, H.W., Wong, R.C.W., Ntoulas, A., Lim, E.P., Ng, S.K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 462–474. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47426-3_36

    Chapter  Google Scholar 

  13. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  14. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, pp. 87–98 (2015)

    Google Scholar 

  15. Jie, H., Li, S., Gang, S., Albanie, S.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 99–111 (2017)

    Google Scholar 

  16. Han, L., Chen, Y., Li, J., Zhong, B., Sun, M.: Liver segmentation with 2.5D perpendicular UNets. Comput. Electr. Eng. 91(6), 107118 (2021)

    Google Scholar 

  17. Bi, L., Kim, J., Kumar, A., Feng, D.: Automatic liver lesion detection using cascaded deep residual networks. arXiv:1704.02703, 2017

Download references

Author information

Authors and Affiliations

  1. Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Xiang Zheng & Chaoli Wang

Authors
  1. Xiang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaoli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaoli Wang .

Editor information

Editors and Affiliations

  1. School of Automatic Science and Electrical Engineering, Beihang University, Beijing, China

    Yingmin Jia

  2. School of Automation and Electrical Engineering, University of Science and Technology, Beijing, China

    Weicun Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Yongling Fu

  4. Beijing Institute of Precision Mechatronics and Controls, Beijing, China

    Zhiyuan Yu

  5. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, China

    Song Zheng

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, X., Wang, C. (2022). A Segmentation Network for CT Image of Hepatocellular Carcinoma Based on Attention Block. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 805. Springer, Singapore. https://doi.org/10.1007/978-981-16-6320-8_25

Download citation

Publish with us

Policies and ethics

Search

Navigation