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Peer-Reviewed Publication
Eur Radiol Exp2020;4(1):26.April 17, 2020Journal Article

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

Sebastian Röhrich1, Thomas Schlegl2, Constanze Bardach1, Helmut Prosch3, Georg Langs4
1Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
2Contextflow GmbH, Vienna, Austria.
3Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria. helmut.prosch@meduniwien.ac.at.
4Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
PubMed PMC DOI PDF

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. METHODS: A deep residual UNet was developed and evaluated for automated…

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