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Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks

  • Farid OuhmichEmail author
  • Vincent Agnus
  • Vincent Noblet
  • Fabrice Heitz
  • Patrick Pessaux
Original Article
  • 107 Downloads

Abstract

Purpose

We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.

Methods

We devise a cascaded convolutional neural network based on the U-Net architecture. Two strategies for dealing with multiphase information are compared: Single-phase images are concatenated in a multi-dimensional features map on the input layer, or output maps are computed independently for each phase before being merged to produce the final segmentation. Each network of the cascade is specialized in the segmentation of a specific tissue. The performances of these networks taken separately and of the cascaded architecture are assessed on both single-phase and on multiphase images.

Results

In terms of Dice coefficients, the proposed method is on par with a state-of-the-art method designed for automatic MR image segmentation and outperforms previously used technique for interactive CT image segmentation. We validate the hypothesis that several cascaded specialized networks have a higher prediction accuracy than a single network addressing all tasks simultaneously. Although the portal venous phase alone seems to provide sufficient contrast for discriminating tumors from healthy parenchyma, the multiphase information brings significant improvement for the segmentation of cancerous tissues (active versus necrotic part).

Conclusion

The proposed cascaded multiphase architecture showed promising performances for the automatic segmentation of liver tissues, allowing to reliably estimate the necrosis rate, a valuable imaging biomarker of the clinical outcome.

Keywords

Hepatocellular carcinoma Multiphase CT Semantic segmentation Fully convolutional networks (FCNs) Liver tissues 

Notes

Funding

This study was funded by IHU Strasbourg through ANR grant 10-IAHU-0002.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

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 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

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

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Nouvel Hôpital CivilInstitut Hospitalo-Universitaire de StrasbourgStrasbourgFrance
  2. 2.ICube UMR 7357University of Strasbourg, CNRS, FMTSIllkirchFrance
  3. 3.Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital CivilInstitut Hospitalo-Universitaire de StrasbourgStrasbourgFrance

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