Benefits of deep learning for delineation of organs at risk in head and neck cancer.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: May 27, 2019

Abstract

PURPOSE/OBJECTIVE: Precise delineation of organs at risk (OARs) in head and neck cancer (HNC) is necessary for accurate radiotherapy. Although guidelines exist, significant interobserver variability (IOV) remains. The aim was to validate a 3D convolutional neural network (CNN) for semi-automated delineation of OARs with respect to 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.
  • S Deschuymer
    KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, Belgium.
  • D Robben
    KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, Belgium; Icometrix, B-3000 Leuven, Belgium.
  • W Crijns
    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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