Abstract
Ejection Fraction (EF) is a widely-used and critical index of cardiac health. EF measures the efficacy of the cyclic contraction of the ventricles and the outward pumpage of blood through the arteries. Timely and robust evaluation of EF is essential, as reduced EF indicates dysfunction in blood delivery during the ventricular systole, and is associated with a number of cardiac and non-cardiac risk factors and mortality-related outcomes. Automated reliable EF estimation in echocardiography (echo) has proven challenging due to low and variable image quality, and limited amounts of data for training data-driven algorithms which delays the integration of the technologies in the clinical workflow. In this paper, we introduce a Bayesian learning framework for automated EF assessment in echo videos. Our key contribution is to automatically estimate the epistemic uncertainty, i.e. the model uncertainty, in EF estimation. We anticipate that such information about uncertainty can be incorporated in clinical decision making. We use a ResNet18-based (2 + 1)D as the baseline architecture for video analysis and provide its side-by-side comparison of our probabilistic approach using public data from 10,031 echo exams. Our results clearly indicate the superior performance of the Bayesian model in the clinically critical lower EF population.
Keywords
This work is funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes of Health Research (CIHR).
M.M. Kazemi Esfeh and C. Luong—Joint first authorship.
T. Tsang and P. Abolmaesumi—Joint senior authorship.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Class 2 device recall Vscan Extend. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfRes/res.cfm?id=173162. Accessed 16 Mar 2020
Everything you need to know about ejection fraction. https://www.healthline.com/health/ejection-fraction#measurement. Accessed 16 Mar 2020
Behnami, D., et al.: Automatic detection of patients with a high risk of systolic cardiac failure in echocardiography. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 65–73. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_8
Behnami, D., et al.: Dual-view joint estimation of left ventricular ejection fraction with uncertainty modelling in echocardiograms. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 696–704. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_77
Gal, Y.: Uncertainty in deep learning. Ph.D. thesis, PhD thesis, University of Cambridge (2016)
Ge, R., et al.: K-net: integrate left ventricle segmentation and direct quantification of paired echo sequence. IEEE TMI 39(5), 1690–1702 (2019)
Ghorbani, A., et al.: Deep learning interpretation of echocardiograms. NPJ Digit. Med. 3(1), 1–10 (2020)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE CVPR, pp. 770–778 (2016)
Jafari, M.H., et al.: A unified framework integrating recurrent fully-convolutional networks and optical flow for segmentation of the left ventricle in echocardiography data. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 29–37. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_4
Jafari, M.H., et al.: Semi-supervised learning for cardiac left ventricle segmentation using conditional deep generative models as prior. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 649–652. IEEE (2019)
Jefferson, A.L., et al.: Relation of left ventricular ejection fraction to cognitive aging (from the framingham heart study). Am. J. Cardiol. 108(9), 1346–1351 (2011)
Jones, N., et al.: Survival of patients with chronic heart failure in the community: a systematic review and meta-analysis. Eur. J. Heart Fail. 21(11), 1306–1325 (2019)
Jordan, M.I., et al.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183–233 (1999)
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision. In: NIPS, pp. 5574–5584 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE TMI 38(9), 2198–2210 (2019)
Lerman, B.J., et al.: Association of left ventricular ejection fraction and symptoms with mortality after elective noncardiac surgery among patients with heart failure. JAMA 321(6), 572–579 (2019)
Litjens, G., et al.: State-of-the-art deep learning in cardiovascular image analysis. JACC Cardiovasc. Imag. 12(8), 1549–1565 (2019)
Ouyang, D., et al.: Interpretable AI for beat-to-beat cardiac function assessment. medRxiv, p. 19012419 (2019)
Pieri, M., et al.: Outcome of cardiac surgery in patients with low preoperative ejection fraction. BMC Anesthesiol. 16(1), 97 (2016)
Ranganath, R., Gerrish, S., Blei, D.: Black box variational inference. In: Artificial Intelligence and Statistics, pp. 814–822 (2014)
Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400–407 (1951)
Snoek, J., et al.: Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift. In: Advances in Neural Information Processing Systems, pp. 13969–13980 (2019)
Tran, D., et al.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE CVPR, pp. 6450–6459 (2018)
Tullio, D., et al.: Left ventricular ejection fraction and risk of stroke and cardiac events in heart failure: data from the warfarin versus aspirin in reduced ejection fraction trial. Stroke 47(8), 2031–2037 (2016)
Wainwright, M.J., et al.: Graphical models, exponential families, and variational inference. Found. Trends\(^{\textregistered }\) Mach. Learn. 1(1–2), 1–305 (2008)
Wingate, D., Weber, T.: Automated variational inference in probabilistic programming (2013). arXiv preprint arXiv:1301.1299
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kazemi Esfeh, M.M., Luong, C., Behnami, D., Tsang, T., Abolmaesumi, P. (2020). A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-59713-9_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59712-2
Online ISBN: 978-3-030-59713-9
eBook Packages: Computer ScienceComputer Science (R0)