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Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training

  • Mohammad H. JafariEmail author
  • Hany Girgis
  • Nathan Van Woudenberg
  • Zhibin Liao
  • Robert Rohling
  • Ken Gin
  • Purang Abolmaesumi
  • Terasa Tsang
Original Article
  • 39 Downloads

Abstract

Purpose

Left ventricular ejection fraction (LVEF) is one of the key metrics to assess the heart functionality, and cardiac ultrasound (echo) is a standard imaging modality for EF measurement. There is an emerging interest to exploit the point-of-care ultrasound (POCUS) usability due to low cost and ease of access. In this work, we aim to present a computationally efficient mobile application for accurate LVEF estimation.

Methods

Our proposed mobile application for LVEF estimation runs in real time on Android mobile devices that have either a wired or wireless connection to a cardiac POCUS device. We propose a pipeline for biplane ejection fraction estimation using apical two-chamber (AP2) and apical four-chamber (AP4) echo views. A computationally efficient multi-task deep fully convolutional network is proposed for simultaneous LV segmentation and landmark detection in these views, which is integrated into the LVEF estimation pipeline. An adversarial critic model is used in the training phase to impose a shape prior on the LV segmentation output.

Results

The system is evaluated on a dataset of 427 patients. Each patient has a pair of captured AP2 and AP4 echo studies, resulting in a total of more than 40,000 echo frames. The mobile system reaches a noticeably high average Dice score of 92% for LV segmentation, an average Euclidean distance error of 2.85 pixels for the detection of anatomical landmarks used in LVEF calculation, and a median absolute error of 6.2% for LVEF estimation compared to the expert cardiologist’s annotations and measurements.

Conclusion

The proposed system runs in real time on mobile devices. The experiments show the effectiveness of the proposed system for automatic LVEF estimation by demonstrating an adequate correlation with the cardiologist’s examination.

Keywords

Mobile application Deep learning Adversarial training Cardiac ejection fraction Image segmentation Echocardiography 

Notes

Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research and Council of Canada (NSERC) and in part by the Canadian Institutes of Health Research (CIHR).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.The University of British ColumbiaVancouverCanada
  2. 2.Vancouver General HospitalVancouverCanada

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