Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.

Journal: European radiology experimental
Published Date: Apr 17, 2020

Abstract

BACKGROUND: Automatically detecting and quantifying pneumothorax on chest computed tomography (CT) may impact clinical decision-making. Machine learning methods published so far struggle with the heterogeneity of technical parameters and the presence of additional pathologies, highlighting the importance of stable algorithms.

Authors

  • Sebastian Röhrich
    Universitätsklinik für Radiologie und Nuklearmedizin, Computational Imaging Research Lab, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich.
  • Thomas Schlegl
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria.
  • Constanze Bardach
    Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Helmut Prosch
    Universitätsklinik für Radiologie und Nuklearmedizin, Computational Imaging Research Lab, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich.
  • Georg Langs
    Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab, Medical University of Vienna Vienna Austria.

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