Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies.

Journal: Investigative radiology
PMID: 33930003

Abstract

PURPOSE: The aims of this study were to train and evaluate deep learning models for automated segmentation of abdominal organs in whole-body magnetic resonance (MR) images from the UK Biobank (UKBB) and German National Cohort (GNC) MR imaging studies and to make these models available to the scientific community for analysis of these data sets.

Authors

  • Turkay Kart
    From the Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK.
  • Marc Fischer
    University of Stuttgart, Institute of Signal Processing and System Theory, Stuttgart, Germany; University of Tübingen, Department of Radiology, Tübingen, Germany.
  • Thomas Küstner
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
  • Tobias Hepp
    University of Tübingen, Department of Radiology, Tübingen, Germany.
  • Fabian Bamberg
    Department of Diagnostic and Interventional Radiology, University Medical Center Tübingen, Tübingen, Germany.
  • Stefan Winzeck
    University Division of Anaesthesia, Department of Medicine, University of Cambridge, United Kingdom (S.W.).
  • Ben Glocker
    Kheiron Medical Technologies, London, UK.
  • Daniel Rueckert
    Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK. Electronic address: d.rueckert@imperial.ac.uk.
  • Sergios Gatidis
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.

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