ArticleApplied sciences (Basel, Switzerland)2021
Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data.
Article in Applied sciences (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- 4D ultrasound-based strain assessment of cardiac dysfunction in male rats with reperfused and nonreperfused myocardial infarction.Physiological reports · 2024Article
- Sex differences in left-ventricular strain in a murine model of coxsackievirus B3 myocarditis.iScience · 2023Article
- Application of 4-D ultrasound-derived regional strain and proteomics analysis inAmerican journal of physiology. Heart and circulatory physiology · 2023Article
- Localized strain characterization of cardiomyopathy in Duchenne muscular dystrophy using novel 4D kinematic analysis of cine cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review.Sensors (Basel, Switzerland) · 2022Review
- Quantification of murine myocardial infarct size using 2-D and 4-D high-frequency ultrasound.American journal of physiology. Heart and circulatory physiology · 2022Article
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4 authors.
Funding
Abstract
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 R
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