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Int J Comput Assist Radiol Surg. 2019 Apr 2. doi: 10.1007/s11548-019-01954-w. [Epub ahead of print]

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

Author information

1
The University of British Columbia, Vancouver, Canada. mohammadj@ece.ubc.ca.
2
The University of British Columbia, Vancouver, Canada.
3
Vancouver General Hospital, Vancouver, Canada.

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:

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

PMID:
30941679
DOI:
10.1007/s11548-019-01954-w

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