Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation.

ABSTRACT: Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When considering large populations, this becomes a time-consuming process, that can be limited by inter-observer variability and operator bias. In this study, we developed and optimized an automated ZC selection algorithm, towards a faster more robust isochrone generation approach. The algorithm either relies on heuristic-based baselines or machine learning classifiers. Manually generated isochrones, previously validated against 3D intracardiac mapping, were considered as ground truth during training and performance evaluation steps. The machine lea

SUBMITTER: Melki L 

PROVIDER: S-EPMC8410624 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature