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Automatic Detection of Extra-Cardiac Findings in Cardiovascular Magnetic Resonance

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)

Abstract

Cardiovascular magnetic resonance (CMR) is an established, non-invasive technique to comprehensively assess cardiovascular structure and function in a variety of acquired and inherited cardiac conditions. In addition to the heart, a typical CMR examination will also image adjacent thoracic and abdominal structures. Consequently, findings incidental to the cardiac examination may be encountered, some of which may be clinically relevant. We compare two deep learning architectures to automatically detect extra cardiac findings (ECFs) in the HASTE sequence of a CMR acquisition. The first one consists of a binary classification network that detects the presence of ECFs and the second one is a multi-label classification network that detects and classifies the type of ECF. We validated the two models on a cohort of 236 subjects, corresponding to 5610 slices, where 746 ECFs were found. Results show that the proposed methods have promising balanced accuracy and sensitivity and high specificity.

Keywords

Deep learning classification Extra cardiac findings Cardiovascular magnetic resonance 

Notes

Acknowledgements

This work was supported by the EPSRC (EP/R005516/1 and EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z).

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  2. 2.Guy’s and St Thomas’ HospitalLondonUK
  3. 3.Luigi Sacco University HospitalMilanItaly
  4. 4.Postgraduate School of Diagnostic and Interventional RadiologyUniversità degli Studi di MilanoMilanItaly

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