A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data

Accurate segmentation of left ventricle (LV) from echocardiograms is a key step toward diagnosis of cardiovascular diseases. Manual segmentation of the LV done by sonographers or cardiologists can be time-consuming, and its accuracy is subjective to the operator’s experience and skill level. Automation of LV segmentation is a challenging task due to a number of factors such as the presence of speckle and a high operator-dependent variability in acquiring echocardiography data. In this paper, we present a method that integrates deep recurrent fully-convolutional networks and optical flow estimation to accurately segment the LV in the apical four-chamber (A4C) view. Our method analyzes the temporal information in echocardiogram cines with the use of convolutional bi-directional long short-term memory units. Furthermore, it uses optical flow motion estimation between consecutive frames to improve the segmentation accuracy. The proposed method is evaluated over an echo cine dataset of 566 patients. Experiments show that the proposed system can reach a noticeably high mean accuracy of 97.9%, and mean Dice score of 92.7% for LV segmentation in A4C view.