Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Apr 20, 2020

Abstract

PURPOSE: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation.

Authors

  • Xikai Tang
    Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium. xikai.tang@kuleuven.be.
  • Esmaeel Jafargholi Rangraz
    Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium.
  • Walter Coudyzer
    Radiology, University Hospitals Leuven, Leuven, Belgium.
  • Jeroen Bertels
    Department of Electrical Engineering - ESAT/PSI, KU Leuven, Leuven, Belgium.
  • David Robben
    Medical Imaging Research Center (MIRC), KU Leuven, Leuven, Belgium; Medical Image Computing (MIC), ESAT-PSI, Department of Electrical Engineering, KU Leuven, Leuven, Belgium; Icometrix, Leuven, Belgium. Electronic address: david.robben@kuleuven.be.
  • Georg Schramm
    Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium.
  • Wies Deckers
    Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium.
  • Geert Maleux
    Radiology, University Hospitals Leuven, Leuven, Belgium.
  • Kristof Baete
    Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium.
  • Chris Verslype
    Digestive Oncology, University Hospitals Leuven, Leuven, Belgium.
  • Mark J Gooding
    2 Mirada Medical Ltd, Oxford, UK.
  • Christophe M Deroose
    Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium.
  • Johan Nuyts
    Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium.

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