Semi-supervised Key-Point Estimation for Echocardiography Video

Echocardiography, a widely used imaging modality, offers real-time assessments of cardiac morphology and function, with a particular emphasis on left ventricular dynamics. Despite its clinical importance, existing automated methods for echocardiographic analysis struggle to ensure temporal consistency in left ventricular key-point trajectories, largely due to their reliance on static frame annotations. To overcome these challenges, we propose a semi-supervised trajectory refinement framework that employs inter-frame correlations to enhance key-point estimation across echocardiography videos. A semi-supervised trajectory learning scheme is presented to improve the efficacy of key-point trajectory analysis using unannotated echocardiography videos. The experiments present considerable improvements in both spatial accuracy and temporal stability of the left ventricle key-point trajectories, outperforming state-of-the-art baselines and demonstrating the clinical applicability for robust echocardiography analysis.