Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.

Journal: Radiation oncology (London, England)
Published Date: Aug 14, 2023

Abstract

BACKGROUND AND PURPOSE: Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of organs-at-risk (OARs), which is also prone to inter- and intra-observer variability. Therefore, deep learning autosegmentation (DLAS) is becoming increasingly attractive. No investigation of its application to OARs in thoracic magnetic resonance images (MRIs) from MRgRT has been done so far. This study aimed to fill this gap.

Authors

  • Marvin F Ribeiro
    Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Sebastian Marschner
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Maria Kawula
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
  • Moritz Rabe
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany.
  • Stefanie Corradini
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Claus Belka
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Marco Riboldi
    Department of Medical Physics, Ludwig-Maximilians-Universität München, Germany.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.

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