Deep learning segmentation of general interventional tools in two-dimensional ultrasound images.

Journal: Medical physics
Published Date: Aug 30, 2020

Abstract

PURPOSE: Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultrasound (US) to provide real-time feedback. Identifying tools in two-dimensional (2D) images can often be time-consuming with the precise position difficult to distinguish. We have developed and implemented a deep learning method to segment tools in 2D US images in near real-time for multiple anatomical sites, despite the widely varying appearances across interventional applications.

Authors

  • Derek J Gillies
    Department of Medical Biophysics, Western University, London, ON, N6A 3K7, Canada.
  • Jessica R Rodgers
    Robarts Research Institute, Western University, London, Ontario, N6A 3K7, Canada.
  • Igor Gyacskov
    Robarts Research Institute, Western University, London, ON, N6A 3K7, Canada.
  • Priyanka Roy
    Department of Medical Biophysics, Western University, London, Ontario, N6A 3K7, Canada.
  • Nirmal Kakani
    Department of Radiology, Manchester Royal Infirmary, Manchester, M13 9WL, UK.
  • Derek W Cool
    Department of Medical Imaging, Western University, London, Ontario, N6A 3K7, Canada.
  • Aaron Fenster
    Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, London, Ontario N6A 5K8, Canada.

Keywords

External Resources

Popular Topics
Recent Journals