Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT.

Journal: Medical physics
Published Date: Aug 27, 2020

Abstract

PURPOSE: Deep learning-based whole-heart segmentation in coronary computed tomography angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable, but defining a manual reference standard that would allow training a deep learning-based method for whole-heart segmentation in NCCT is challenging, if not impossible. In this work, we leverage dual-energy information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a three-dimensional (3D) convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images.

Authors

  • Steffen Bruns
    Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, Amsterdam, 1105 AZ, Netherlands.
  • Jelmer M Wolterink
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • Richard A P Takx
    Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands. Electronic address: r.a.p.takx@umcutrecht.nl.
  • Robbert W van Hamersvelt
    Department of Radiology, University Medical Center Utrecht, E.01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands. Electronic address: r.w.vanhamersvelt-3@umcutrecht.nl.
  • Dominika Suchá
    Department of Radiology, University Medical Center Utrecht, Utrecht, 3584 CX, Netherlands.
  • Max A Viergever
  • Tim Leiner
    Departments of Radiology and Nuclear Medicine (C.P.S.B., A.J.N., P.v.O., R.N.P.) and Cardiology (S.M.B.), Amsterdam University Medical Centers, Location Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (J.J.M.W.); Department of Research and Development, Pie Medical Imaging BV, Maastricht, the Netherlands (J.P.A.); and Departments of Cardiology (G.P.B., S.A.J.C.) and Radiology (T.L.), University Medical Center Utrecht, Utrecht, the Netherlands.
  • Ivana Išgum
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.

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