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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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Funding

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

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Authors and Affiliations

  1. Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l’Hôpital, 67000, Strasbourg, France

    Farid Ouhmich & Vincent Agnus

  2. ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France

    Vincent Noblet & Fabrice Heitz

  3. Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l’Hôpital, 67000, Strasbourg, France

    Patrick Pessaux

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  1. Farid Ouhmich
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  2. Vincent Agnus
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  3. Vincent Noblet
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  4. Fabrice Heitz
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  5. Patrick Pessaux
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Corresponding author

Correspondence to Farid Ouhmich.

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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.

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Ouhmich, F., Agnus, V., Noblet, V. et al. Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks. Int J CARS 14, 1275–1284 (2019). https://doi.org/10.1007/s11548-019-01989-z

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  • DOI: https://doi.org/10.1007/s11548-019-01989-z

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