Open-source deep-learning models for segmentation of normal structures for prostatic and gynecological high-dose-rate brachytherapy: Comparison of architectures.

Journal: Journal of applied clinical medical physics
Published Date: Apr 5, 2025

Abstract

BACKGROUND: The use of deep learning-based auto-contouring algorithms in various treatment planning services is increasingly common. There is a notable deficit of commercially or publicly available models trained on large or diverse datasets containing high-dose-rate (HDR) brachytherapy treatment scans, leading to poor performance on images that include HDR implants.

Authors

  • Andrew J Krupien
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • Yasin Abdulkadir
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • Dishane C Luximon
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • John Charters
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • Huiming Dong
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • Jonathan Pham
    Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America.
  • Dylan O'Connell
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • Jack Neylon
    Department of Radiation Oncology, University of California, Los Angeles, California, USA.
  • James M Lamb
    Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, USA.

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