PSMA-PET improves deep learning-based automated CT kidney segmentation.

Journal: Zeitschrift fur medizinische Physik
Published Date:

Abstract

UNLABELLED: For dosimetry of radiopharmaceutical therapies, it is essential to determine the volume of relevant structures exposed to therapeutic radiation. For many radiopharmaceuticals, the kidneys represent an important organ-at-risk. To reduce the time required for kidney segmentation, which is often still performed manually, numerous approaches have been presented in recent years to apply deep learning-based methods for CT-based automated segmentation. While the automatic segmentation methods presented so far have been based solely on CT information, the aim of this work is to examine the added value of incorporating PSMA-PET data in the automatic kidney segmentation.

Authors

  • Julian Leube
    University Hospital Würzburg, Department of Nuclear Medicine, Oberdürrbacher Str. 6, 97080 Würzburg, Germany. Electronic address: Leube_J@ukw.de.
  • Matthias Horn
    University Hospital Würzburg, Department of Nuclear Medicine, Oberdürrbacher Str. 6, 97080 Würzburg, Germany.
  • Philipp E Hartrampf
    University Hospital Würzburg, Department of Nuclear Medicine, Oberdürrbacher Str. 6, 97080 Würzburg, Germany.
  • Andreas K Buck
    University Hospital Würzburg, Department of Nuclear Medicine, Oberdürrbacher Str. 6, 97080 Würzburg, Germany.
  • Michael Lassmann
    Klinik Und Poliklinik Für Nuklearmedizin, Universitätsklinikum Würzburg, Würzburg, Germany.
  • Johannes Tran-Gia
    Department of Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany.