Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.

Journal: Journal of medical radiation sciences
Published Date: Sep 23, 2022

Abstract

INTRODUCTION: Contouring organs at risk (OARs) is a time-intensive task that is a critical part of radiation therapy. Atlas-based automatic segmentation has shown some success at reducing this time burden on practitioners; however, this method often requires significant manual editing to reach a clinically accurate standard. Deep learning (DL) auto-segmentation has recently emerged as a promising solution. This study compares the accuracy of DL and atlas-based auto-segmentation in relation to clinical 'gold standard' reference contours.

Authors

  • Eddie Gibbons
    Department of Radiation Oncology, Mid North Coast Cancer Institute, Port Macquarie, New South Wales, Australia.
  • Matthew Hoffmann
    Department of Radiation Oncology, Mid North Coast Cancer Institute, Port Macquarie, New South Wales, Australia.
  • Justin Westhuyzen
    Department of Radiation Oncology, Mid North Coast Cancer Institute, Coffs Harbour, New South Wales, Australia.
  • Andrew Hodgson
    Department of Radiation Oncology, Mid North Coast Cancer Institute, Port Macquarie, New South Wales, Australia.
  • Brendan Chick
    Department of Radiation Oncology, Mid North Coast Cancer Institute, Port Macquarie, New South Wales, Australia.
  • Andrew Last
    Department of Radiation Oncology, Mid North Coast Cancer Institute, Port Macquarie, New South Wales, Australia.

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