Segmentation of Left Ventricular Endocardium Using Direction-constrained Reinforcement Learning

Accurate segmentation of the left ventricular endocardium from cardiac magnetic resonance imaging to obtain the left ventricular region is an important step in the analysis of cardiac function. It is noted that reinforcement learning is prone to localization deviation in left ventricular endocardium segmentation by locating the left ventricular endocardial edges, leading to a performance decrease in the segmentation. To address this, this paper proposes a direction-constrained reinforcement learning method for left ventricular endocardium segmentation, which divides the segmentation task into two stages. In the first stage, the proposed method extracts global edge features of the endocardium, and in the second stage, reinforcement learning is used to iteratively locate the endocardial edge points to obtain the edge, obtaining the segmented left ventricular endocardium. The proposed method constrains the direction of agent positioning, which can reduce the localization deviation and overlap, such that the segmentation accuracy can be improved. Finally, the experimental results on two public datasets, including the Automated Cardiac Diagnosis Challenge (ACDC) and Sunnybrook Cardiac MR Left Ventricle Segmentation Challenge (Sunnybrook) , show that the proposed method has higher accuracy than the compared methods. Specifically, the F1-score of the proposed method are 0.9482 and 0.9387, and the Average perpendicular distance (APD) are 3.5863 and 4.9447, which can effectively segment the left ventricular endocardium.