General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.

Journal: Medical physics
Published Date: Feb 7, 2022

Abstract

PURPOSE: To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-established deep convolutional neural network (DCNN).

Authors

  • Asma Amjad
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Jiaofeng Xu
    Elekta Inc, Missouri, USA.
  • Dan Thill
    Elekta Inc, Missouri, USA.
  • Colleen Lawton
    Medical College of Wisconsin, Milwaukee, Wisconsin.
  • William Hall
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Musaddiq J Awan
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Monica Shukla
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Beth A Erickson
    Department of Radiation Oncology, Medical College of Wisconsin and Clement J. Zablocki VA Medical Center, Milwaukee, Wisconsin.
  • X Allen Li
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

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