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A machine learning solution was investigated to fully automate the segmentation of the left ventricle and myocardium on highly heterogeneous 2D echocardiographic data.
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A machine learning solution was investigated to fully automate the segmentation of the myocardium on heterogeneous 2D echocardiographic datasets, aiming to reduce the need for manual interactions.
Although 3D ultrasound plays an increasingly important role, 2D echocardiography remains the main clinical imaging modality for cardiac function assessment in daily practice. This requires precise delineation of the myocardium at end diastole (ED) and systole (ES). Because of intrinsic high variability in image quality, manual interactions are still needed. In this study, we investigate a machine learning solution to fully automate the segmentation of the myocardium on heterogeneous dataset.
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Leclerc et al. (2017) studied Cardiac function assessment. Machine learning segmentation was evaluated on Myocardium segmentation. A machine learning solution was investigated to fully automate the segmentation of the left ventricle and myocardium on highly heterogeneous 2D echocardiographic data.
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