A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.

Journal: Medical physics
Published Date: May 1, 2019

Abstract

PURPOSE: Real-time image-guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three-dimensional (3D) motion based on two-dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior knowledge of marker properties to construct a template. If marker properties are not known, an additional learning period is required to build the template which exposes the patient to an additional imaging dose. This work investigates a deep learning-based fiducial marker classifier for use in real-time IGART that requires no prior patient-specific data or additional learning periods. The proposed tracking system uses convolutional neural network (CNN) models to segment cylindrical and arbitrarily shaped fiducial markers.

Authors

  • Adam Mylonas
    Faculty of Medicine and Health, ACRF Image X Institute, The University of Sydney, Sydney, NSW, Australia.
  • Paul J Keall
    Image X Institute, University of Sydney, Sydney, Australia. Electronic address: paul.keall@sydney.edu.au.
  • Jeremy T Booth
    Royal North Shore Hospital, Northern Sydney Cancer Centre, St Leonards, NSW, Australia.
  • Chun-Chien Shieh
    Faculty of Medicine and Health, ACRF Image X Institute, The University of Sydney, Sydney, NSW, Australia.
  • Thomas Eade
    Royal North Shore Hospital, Northern Sydney Cancer Centre, St Leonards, NSW, Australia.
  • Per Rugaard Poulsen
    Department of Oncology, Aarhus University Hospital, 8000, Aarhus, Denmark.
  • Doan Trang Nguyen
    Faculty of Medicine and Health, ACRF Image X Institute, The University of Sydney, Sydney, NSW, Australia.

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