Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.

Journal: Medical physics
PMID: 39699250

Abstract

BACKGROUND: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.

Authors

  • Maria Kawula
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
  • Sebastian Marschner
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Chengtao Wei
    Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Marvin F Ribeiro
    Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, 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.
  • 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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