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Automated liver segmentation from a postmortem CT scan based on a statistical shape model

  • Atsushi SaitoEmail author
  • Seiji Yamamoto
  • Shigeru Nawano
  • Akinobu Shimizu
Original Article

Abstract

Purpose

Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver.

Methods

The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation–maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label.

Results

The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference.

Conclusions

We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.

Keywords

Liver segmentation Postmortem CT Statistical shape model EM algorithm Autopsy imaging Synthesized-based learning 

Notes

Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Number 15J08775 and MEXT KAKENHI Grant Number 26108002.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki declaration, as revised in 2008(5).

Informed consent

For this type of study, formal consent is not required.

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Copyright information

© CARS 2016

Authors and Affiliations

  • Atsushi Saito
    • 1
    Email author
  • Seiji Yamamoto
    • 2
  • Shigeru Nawano
    • 3
  • Akinobu Shimizu
    • 1
  1. 1.Tokyo University of Agriculture and TechnologyKoganeiJapan
  2. 2.Ai Information CenterChuo-kuJapan
  3. 3.Department of RadiologyInternational University of Health and Welfare, Mita HospitalMinato-kuJapan

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