Deep learning for elective neck delineation: More consistent and time efficient.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Oct 14, 2020

Abstract

BACKGROUND/PURPOSE: Delineation of the lymph node levels of the neck for irradiation of the elective clinical target volume in head and neck cancer (HNC) patients is time consuming and prone to interobserver variability (IOV), although international consensus guidelines exist. The aim of this study was to develop and validate a 3D convolutional neural network (CNN) for semi-automated delineation of all nodal neck levels, focussing on delineation accuracy, efficiency and consistency compared to manual delineation.

Authors

  • J van der Veen
    KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, Belgium.
  • S Willems
    KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, Belgium.
  • H Bollen
    KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, Belgium.
  • F Maes
    KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, Belgium.
  • S Nuyts
    KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, Belgium. Electronic address: sandra.nuyts@uzleuven.be.

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