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Article

Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data

1
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
3
Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
*
Authors to whom correspondence should be addressed.
Frederick W. Damen and David T. Newton have contributed equally.
Academic Editor: Mauro Castelli
Appl. Sci. 2021, 11(4), 1690; https://doi.org/10.3390/app11041690
Received: 20 December 2020 / Revised: 29 January 2021 / Accepted: 9 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)
Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models. View Full-Text
Keywords: echocardiography; 4D ultrasound; volumetric imaging; murine; left ventricle; myocardium; cardiac kinematics; hypertrophic cardiomyopathy; boundary prediction; machine learning echocardiography; 4D ultrasound; volumetric imaging; murine; left ventricle; myocardium; cardiac kinematics; hypertrophic cardiomyopathy; boundary prediction; machine learning
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MDPI and ACS Style

Damen, F.W.; Newton, D.T.; Lin, G.; Goergen, C.J. Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data. Appl. Sci. 2021, 11, 1690. https://doi.org/10.3390/app11041690

AMA Style

Damen FW, Newton DT, Lin G, Goergen CJ. Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data. Applied Sciences. 2021; 11(4):1690. https://doi.org/10.3390/app11041690

Chicago/Turabian Style

Damen, Frederick W., David T. Newton, Guang Lin, and Craig J. Goergen 2021. "Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data" Applied Sciences 11, no. 4: 1690. https://doi.org/10.3390/app11041690

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