Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts.

Purpose: We present a deep-learning-based methodology for estimating deformation in 2D echocardiograms. The goal is to automatically estimate the longitudinal strain of the left ventricle (LV) walls in images affected by speckle noise and acoustic occlusions. Approach: The proposed methodology integrates algorithms for converting sparse to dense flow, a Res-UNet architecture for automatic myocardium segmentation, flow estimation using a global motion aggregation network, and the computation of longitudinal strain curves and the global longitudinal strain (GLS) index. The approach was evaluated using two echocardiographic datasets in apical four-chamber view, both modified with noise and acoustic shadows. The CAMUS dataset ( Results: Among the main performance metrics achieved are 98% [96 to 99] of correlation in the conversion from sparse to dense flow, a Dice index of Conclusions: The results demonstrate improvements over previously reported performances while maintaining stability in echocardiograms with acoustic shadows. This methodology could be useful in clinical practice for the analysis of echocardiograms with noise artifacts and acoustic occlusions. Our code and trained models are publicly available at https://github.com/ArBioIIMAS/echo-gma.