Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators.

Journal: Practical radiation oncology
PMID: 38325548

Abstract

PURPOSE: The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions for intact prostate and prostate bed treatments.

Authors

  • Riley C Tegtmeier
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Christopher J Kutyreff
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Jennifer L Smetanick
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Dean Hobbis
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Brady S Laughlin
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA.
  • Diego A Santos Toesca
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Edward L Clouser
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Yi Rong
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.

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