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Echocardiography plays an important part in diagnostic aid in cardiacdiseases. A critical step in echocardiography-aided diagnosis is to extract thestandard planes since they tend to provide promising views to present differentstructures that are benefit to diagnosis. To this end, this paper proposes aspatial-temporal embedding framework to extract the standard view planes from4D STIC (spatial-temporal image corre- lation) volumes. The proposed method iscomprised of three stages, the frame smoothing, spatial-temporal embedding andfinal classification. In first stage, an L 0 smoothing filter is used topreprocess the frames that removes the noise and preserves the boundary. Then acompact repre- sentation is learned via embedding spatial and temporal featuresinto a latent space in the supervised scheme considering both standard planeinformation and diagnosis result. In last stage, the learned features are fedinto support vector machine to identify the standard plane. We eval- uate theproposed method on a 4D STIC volume dataset with 92 normal cases and 93abnormal cases in three standard planes. It demonstrates that our methodoutperforms the baselines in both classification accuracy and computationalefficiency.

4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation