Deep-learning-based fully automatic spine centerline detection in CT data.

Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date: Jul 1, 2019

Abstract

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.

Authors

  • Roman Jakubicek
    Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Technicka 3082/12, 616 00, Czechia.
  • Jiri Chmelik
    Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Technicka 3082/12, 616 00, Czechia. Electronic address: chmelikj@feec.vutbr.cz.
  • Petr Ourednicek
    Philips Healthcare, AE Eindhoven, High Tech Campus 34, 5656, Netherlands; Department of Medical Imaging, St. Anne's University Hospital Brno and Faculty of Medicine Masaryk University Brno, Brno, Pekarska 663/53, 656 91 Czechia.
  • Jiri Jan
    Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Technicka 3082/12, 616 00, Czechia.

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