Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Oct 22, 2019

Abstract

INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive evaluation methodology to investigate the performance of HN OAR auto-contouring when using deep learning contouring (DLC), compared to ABAS.

Authors

  • Lisanne V van Dijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: l.v.van.dijk@umcg.nl.
  • Lisa Van den Bosch
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Paul Aljabar
    Centre for the Developing Brain, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Devis Peressutti
    Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom. Electronic address: devis.1.peressutti@kcl.ac.uk.
  • Stefan Both
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Roel J H M Steenbakkers
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Johannes A Langendijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Mark J Gooding
    2 Mirada Medical Ltd, Oxford, UK.
  • Charlotte L Brouwer
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.

Keywords

External Resources

Popular Topics
Recent Journals