Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Aug 31, 2019

Abstract

PURPOSE: This study investigated the potential of deep convolutional neural networks (CNN) for automatic classification of FP-CIT SPECT in multi-site or multi-camera settings with variable image characteristics.

Authors

  • Markus Wenzel
    Fraunhofer Heinrich Hertz Institute and Technische Universität Berlin, Berlin 10587, Germany.
  • Fausto Milletari
  • Julia Krüger
    Jung diagnostics, Hamburg, Germany.
  • Catharina Lange
    Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
  • Michael Schenk
    Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
  • Ivayla Apostolova
    Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
  • Susanne Klutmann
    Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
  • Marcus Ehrenburg
    Pinax Pharma, Bad Liebenwerda, Germany.
  • Ralph Buchert
    Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. r.buchert@uke.de.

Keywords

External Resources

Popular Topics
Recent Journals