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Proceedings Paper

Robust chest x-ray quality assessment using convolutional neural networks and atlas regularization
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Paper Abstract

The quality of chest radiographs is a practical issue because deviations from quality standards cost radiologists' time, may lead to misdiagnosis and hold legal risks. Automatic and reproducible assessment of the most important quality figures on every acquisition can enable a radiology department to measure, maintain, and improve quality rates on an everyday basis. A method is proposed here to automatically quantify the quality according to the aspects of (i) collimation, (ii) patient rotation, and (iii) inhalation state of a chest PA radiograph by localizing a number of anatomical features and calculating some quality figures in accordance with international standards. The anatomical features related to these quality aspects are robustly detected by a combination of three convolutional neural networks and two probabilistic anatomical atlases. An error analysis demonstrates the accuracy and robustness of the method. The implementation proposed here works in real time (less than a second) on a CPU without any GPU support.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131L (10 March 2020); doi: 10.1117/12.2549541
Show Author Affiliations
Jens von Berg, Philips Research (Germany)
Sven Krönke, Philips Research (Germany)
André Gooßen, Philips Research (Germany)
Daniel Bystrov, Philips Research (Germany)
Matthias Brück, Philips Research (Germany)
Tim Harder, Philips Research (Germany)
Nataly Wieberneit, Philips Healthcare (Germany)
Stewart Young, Philips Research (Germany)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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