A Fast Method to Segment the Liver According to Couinaud’s Classification | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Fast Method to Segment the Liver According to Couinaud’s Classification

  • Conference paper
Medical Imaging and Informatics (MIMI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4987))

Included in the following conference series:

Abstract

For establishing a plan of Living Donor Liver Transplantation (LDLT), it is very important to estimate the volume of each liver segment. Usually Couinaud’s classification is used to segment a liver, which is based on the liver anatomy. However, it is not easy to perform this method in a 3D space directly. In this paper, a fast segment method based on the hepatic vessel tree was proposed. This method was composed of four main steps: vasculature segmentation, 3D thinning, vascular tree pruning and classification, and vascular projection and curve fitting. This method was validated by application to a 3D liver from CT data, and it was shown to approximate closely Couinaud’s classification with high speed.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Couinaud, C.: Liver anatomy: portal (and suprahepatic) or biliary segmentation. Digest Surg. 16, 459–467 (1999)

    Article  Google Scholar 

  2. Lam, L., Lee, S.-W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE transactions on pattern analysis and machine intelligence 14, 869–883 (1992)

    Article  Google Scholar 

  3. Lee, T.-C., Kashyap, R.L.: Building skeleton models via 3-D medial surface/axis thinning algorithm. Graph. Models Image Processing 56, 462–478 (1994)

    Article  Google Scholar 

  4. Min, X., et al.: Study of the surgical anatomu and the volume of the liver in man. Chinese journal of general surgery 5, 1–3 (1996)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Xiamen University, 361005, Xiamen, Fujian, China

    Shao-hui Huang, Bo-liang Wang, Ming Cheng, Wei-li Wu, Xiao-yang Huang & Ying Ju

Authors
  1. Shao-hui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo-liang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei-li Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiao-yang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ying Ju
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Xiaohong Gao Henning Müller Martin J. Loomes Richard Comley Shuqian Luo

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Huang, Sh., Wang, Bl., Cheng, M., Wu, Wl., Huang, Xy., Ju, Y. (2008). A Fast Method to Segment the Liver According to Couinaud’s Classification. In: Gao, X., Müller, H., Loomes, M.J., Comley, R., Luo, S. (eds) Medical Imaging and Informatics. MIMI 2007. Lecture Notes in Computer Science, vol 4987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79490-5_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-79490-5_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79489-9

  • Online ISBN: 978-3-540-79490-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation