Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

Journal: Radiation oncology (London, England)
Published Date: May 11, 2020

Abstract

BACKGROUND: Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant. However, the majority of the studies investigated automatic contouring based on computed tomography (CT).

Authors

  • Mark H F Savenije
    Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Matteo Maspero
    Department of Radiation Oncology, Imaging and Cancer Division, University Medical Center Utrecht, Utrecht, The Netherlands; Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Gonda G Sikkes
    Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands.
  • Jochem R N van der Voort van Zyp
    1 Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands.
  • Alexis N T J Kotte
    Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands.
  • Gijsbert H Bol
    Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands.
  • Cornelis A T van den Berg
    Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands.

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