A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

Journal: Radiation oncology (London, England)
Published Date: Jul 22, 2022

Abstract

BACKGROUND: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

Authors

  • Sebastian Marschner
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Manasi Datar
    Technology Excellence, Digital Technology & Innovation, Siemens Healthineers, Erlangen, Germany.
  • Aurélie Gaasch
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
  • Zhoubing Xu
    Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA. Electronic address: zhoubing.xu@vanderbilt.edu.
  • Sasa Grbic
  • Guillaume Chabin
    Technology Excellence, Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA.
  • Bernhard Geiger
    Technology Excellence, Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA.
  • Julian Rosenman
    Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Stefanie Corradini
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Maximilian Niyazi
    Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Tobias Heimann
  • Christian Möhler
    Cancer Therapy, Siemens Healthineers, Forchheim, Germany.
  • Fernando Vega
    Cancer Therapy, Siemens Healthineers, Forchheim, Germany.
  • Claus Belka
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Christian Thieke
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.

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