Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging.

Journal: Medical physics
Published Date: Nov 1, 2021

Abstract

PURPOSE: To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time-consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto-segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk.

Authors

  • James C Korte
    Department of Physical Science, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
  • Nicholas Hardcastle
    Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
  • Sweet Ping Ng
    Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
  • Brett Clark
    Department of Physical Science, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
  • Tomas Kron
    Department of Oncology, Sir Peter MacCallum, University of Melbourne, Melbourne, Victoria, Australia.
  • Price Jackson
    Department of Oncology, Sir Peter MacCallum, University of Melbourne, Melbourne, Victoria, Australia.

Keywords

External Resources

Popular Topics
Recent Journals