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Liver segmentation from low-radiation-dose pediatric computed tomography using patient-specific, statistical modeling

  • Koyo NakayamaEmail author
  • Atsushi Saito
  • Elijah Biggs
  • Marius George Linguraru
  • Akinobu Shimizu
Original Article
  • 18 Downloads

Abstract

Purpose

The pediatric computed tomography (CT) volume is acquired at a low dose because radiation is harmful to young children. Consequently, the pediatric CT volume has lower signal-to-noise ratio, which makes organ segmentation difficult. In this paper, we propose a liver segmentation algorithm for pediatric CT scan using a patient-specific level set distribution model (LSDM).

Methods

The patient-specific LSDM was constructed using a conditional LSDM (C-LSDM) conditioned on age. Furthermore, a patient-specific probabilistic atlas (PA) was generated using the model, which became a priori to the maximum a posteriori-based segmentation. The patient-specific PA generation by the C-LSDM using kernel density estimation was quicker than the conventional PA generation method using random numbers, and also, it was more accurate as it did not include any approximations.

Results

The liver segmentation algorithm was tested on 42 CT volumes of children aged between 2 weeks and 7 years. In the proposed method, the calculation time of the PA was about 9 s for the single Gaussian method, while it was 337 s for the conventional PA generation method using random numbers. Furthermore, using the kernel density estimation, median and 25%/75% tile of the generalized Dice similarity index between the PA and the correct liver region were found to be 0.3443 and 0.3191/0.3595. The Dice similarity index in the segmentation was 0.8821 and 0.8545/0.9085, which are higher than those obtained by the conventional method, and requires lower computational cost.

Conclusion

We proposed a method to quickly and accurately generate a PA, combined with C-LSDM using kernel density estimation, which enabled efficient calculation and improved segmentation accuracy.

Keywords

Pediatrics Computed tomography Liver segmentation Conditional statistical shape model Patient-specific probabilistic atlas 

Notes

Acknowledgements

This work was partly supported by KAKENHI (Nos. 26108002 and 18H03255) and the Sheikh Zayed Institute at Children’s National Health System in Washington, DC, USA.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All the procedures of the study with human participants were performed in compliance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki Declaration, as revised in 2008(5). The study was approved by the Ethics Committee at Children’s National Medical Center (Approval No. 00003792) and Tokyo University of Agriculture and Technology (Approval No. 30-31).

Informed consent

Informed consent was obtained from all the participants included in the study.

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Tokyo University of Agriculture and TechnologyKoganeiJapan
  2. 2.Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National Health SystemWashingtonUSA

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