Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

Journal: Clinical imaging
Published Date: Sep 11, 2018

Abstract

PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.

Authors

  • Joel Aissa
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany. Electronic address: Joel.Aissa@med.uni-duesseldorf.de.
  • Benedikt Michael Schaarschmidt
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany. Electronic address: Benedikt.Schaarschmidt@med.uni-duesseldorf.de.
  • Janina Below
    University Dusseldorf, Medical Faculty, Clinic of Dermatology, Moorenstr. 5, D-40225 Dusseldorf, Germany.
  • Oliver Th Bethge
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Judith Böven
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Lino Morris Sawicki
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Norman-Philipp Hoff
    University Dusseldorf, Medical Faculty, Clinic of Dermatology, Moorenstr. 5, D-40225 Dusseldorf, Germany.
  • Patric Kröpil
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Gerald Antoch
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Johannes Boos
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.

Keywords

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