Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy.

Journal: Brachytherapy
Published Date: Apr 11, 2022

Abstract

PURPOSE: To automate the segmentation of treatment applicators on computed tomography (CT) images for high-dose-rate (HDR) brachytherapy prostate patients implanted with titanium needles with the goals of improving plan quality and reducing the patient's time under anesthesia.

Authors

  • Luca Leon Weishaupt
    Radiation Oncology, Mayo Clinic, Rochester, MN; Medical Physics Unit, McGill University, Montreal, Quebec, Canada.
  • Hisham Kamal Sayed
    Radiation Oncology, Mayo Clinic, Rochester, MN; Inova Schar Cancer Institute, Fairfax, VA.
  • Ximeng Mao
    Department of Computer Science, McGill University, Montréal, Québec, Canada; Medical Physics Unit, McGill University, Montréal, Québec, Canada; Mila-Quebec Artificial Intelligence Institute, Montréal, Québec, Canada. Electronic address: ximeng.mao@mail.mcgill.ca.
  • Richard Choo
    Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota.
  • Bradley J Stish
    Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota.
  • Shirin A Enger
    Medical Physics Unit, McGill University, Montréal, Québec, Canada; Department of Oncology, McGill University, Montréal, Québec, Canada; Research Institute of the McGill University Health Centre, Montréal, Québec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
  • Christopher Deufel
    Radiation Oncology, Mayo Clinic, Rochester, MN. Electronic address: deufel.christopher@mayo.edu.

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