Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

Journal: BMC medical imaging
PMID: 33849483

Abstract

BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches.

Authors

  • Andra-Iza Iuga
    Institute for Diagnostic and Interventional Radiology, University Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany. Electronic address: andra.iuga@uk-koeln.de.
  • Heike Carolus
    Philips Research Hamburg, Hamburg, Germany.
  • Anna J Höink
    Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
  • Tom Brosch
    MS/MRI Research Group, Vancouver, BC V6T 2B5, Canada, and Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada brosch.tom@gmail.com.
  • Tobias Klinder
    Philips Research, Hamburg, 22335, Germany.
  • David Maintz
    Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.
  • Thorsten Persigehl
    Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
  • Bettina Baeßler
    Department of Radiology, University Hospital of Cologne, Cologne, Germany.
  • Michael Püsken
    Institute for Diagnostic and Interventional Radiology, University Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.

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