Semi-supervised learning improves the performance of cardiac event detection in echocardiography

Abstract

Detection of end-diastole (ED) and end-systole (ES) frames in echocardiography video is a critical step for assessment of cardiac function. A recently released large public dataset, i.e., EchoNet-Dynamic, could be used as a benchmark for cardiac event detection. However, only a pair of ED and ES frames are annotated in each echocardiography video and the annotated ED comes before ES in most cases. This means that only a few frames during systole in each video are utilizable for training, which makes it challenging to train a cardiac event detection model using the dataset. Semi-supervised learning (SSL) could alleviate the problems. An architecture combining convolutional neural network (CNN), recurrent neural network (RNN) and fully-connected layers (FC) is adopted. Experimental results indicate that SSL brings at least three benefits: faster convergence rate, performance improvement and more reasonable volume curves. The best mean absolute errors (MAEs) for ED and ES detection are 40.2 ms (2.1 frames) and 32.6 ms (1.7 frames), respectively. In addition, the results show that models trained on apical four-chamber (A4C) view could work well on other standard views, such as other apical views and parasternal short axis (PSAX) views.