Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVES: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs.

Authors

  • Felix Busch
    Institute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
  • Lina Xu
    Emergency Department, Beichen Hospital, Tianjin, China.
  • Dmitry Sushko
    Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
  • Matthias Weidlich
    Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
  • Daniel Truhn
    Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).
  • Gustav Müller-Franzes
    From the Department of Diagnostic and Interventional Radiology (F.K., G.M.F., L.H., P.S., S.K., E.B., M.S.H., F.P., M.Z., C.K., P.B., S.N., D.T.), Department of Medicine III (J.K., K.H.), and Clinic for Surgical Intensive Medicine and Intermediate Care (G.M.), University Hospital Aachen, Pauwelsstrasse 30, 52064 Aachen, Germany; Physics of Molecular Imaging Systems, Experimental Molecular Imaging (T.H., V.S.), and Institute of Imaging and Computer Vision (J.S.), RWTH Aachen University, Aachen, Germany; Department of Inner Medicine, Luisenhospital Aachen, Aachen, Germany (L.N.); and Ocumeda AG, Erlen, Switzerland (C.H.).
  • Maurice M Heimer
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Stefan M Niehues
    Charité Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany.
  • Marcus R Makowski
    School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
  • Markus Hinsche
    Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
  • Janis L Vahldiek
    Charité Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany.
  • Hugo Jwl Aerts
    Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands.
  • Lisa C Adams
    School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
  • Keno K Bressem
    School of Medicine and Health, Institute for Cardiovascular Radiology and Nuclear Medicine, German Heart Center Munich, TUM University Hospital, Technical University of Munich, Munich, Germany.