Incorporating indirect MRI information in a CT-based deep learning model for prostate auto-segmentation.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 39988305

Abstract

BACKGROUND AND PURPOSE: Computed tomography (CT) imaging poses challenges for delineation of soft tissue structures for prostate cancer external beam radiotherapy. Guidelines require the input of magnetic resonance imaging (MRI) information. We developed a deep learning (DL) prostate and organ-at-risk contouring model designed to find the MRI-truth in CT imaging.

Authors

  • Daan Stas
    Department of Radiation Oncology, Iridium Network, Antwerp, Belgium; Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium. Electronic address: daan.stas@gmail.com.
  • Geert De Kerf
    Department of Radiation Oncology, Iridium Network, Wilrijk (Antwerp), Belgium.
  • Michaël Claessens
    Faculty of Medicine and Health Sciences, University of Antwerp, Belgium; Department of Radiation Oncology, Iridium Cancer Network, Wilrijk (Antwerp), Belgium. Electronic address: michael.claessens@uantwerpen.be.
  • Anna Karlhede
    RaySearch Laboratories, Stockholm, Sweden.
  • Jonas Söderberg
    RaySearch Laboratories, Stockholm, Sweden.
  • Piet Dirix
    Department of Radiation Oncology, Iridium Network, Wilrijk (Antwerp), Belgium.
  • Piet Ost
    Department of Human Structure and Repair, Ghent University, Ghent, Belgium; Department of Radiation Oncology, Iridium Network, Antwerp, Belgium. Electronic address: piet.ost@ugent.be.

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