Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

Journal: Medical physics
Published Date: Jun 22, 2021

Abstract

PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reducing planning times. This technical note describes our participation in the American Association of Physicists in Medicine Open Knowledge-Based Planning Challenge (OpenKBP), a competition to accurately predict radiation therapy dose distributions.

Authors

  • Mary P Gronberg
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
  • Skylar S Gay
    Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Tucker J Netherton
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
  • Dong Joo Rhee
  • Laurence E Court
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Carlos E Cardenas
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas. Electronic address: cecardenas@mdanderson.org.

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