An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

• DOI: 10.1007/s11548-016-1429-9
• Publication Date: 2016-06-13T19:11:54Z
• Authors: Yurun Ma; Li Wang; Yide Ma; Min Dong; Shiqiang Du; Xiaoguang Sun
• Keywords: Heart Ventricles; Image Interpretation, Computer-Assisted; 0202 electrical engineering, electronic engineering, information engineering; Humans; Magnetic Resonance Imaging, Cine; Reproducibility of Results; Neural Networks, Computer; 02 engineering and technology; Algorithms; Ventricular Function, Left

Accurate segmentation of left ventricle (LV) is essential for the cardiac function analysis. However, it is labor intensive and time consuming for radiologists to delineate LV boundary manually. In this paper, we present a novel self-correcting framework for the fully automatic LV segmentation.Firstly, a time-domain method is designed to extract a rectangular region of interest around the heart. Then, the simplified pulse-coupled neural network (SPCNN) is employed to locate the LV cavity. Different from the existing approaches, SPCNN can realize the self-correcting segmentation due to its parameter controllability. Subsequently, the post-processing based on the maximum gradient searching is proposed to obtain the accurate endocardium. Finally, a new external force based on the shape similarity is defined and integrated into the gradient vector flow (GVF) snake with the balloon force to segment the epicardium.We obtain encouraging segmentation results tested on the database provided by MICCAI 2009. The average percentage of good contours is 92.26 %, the average perpendicular distance is 2.38 mm, and the overlapping dice metric is 0.89. Besides, the experiment results show good correlations between the automatic segmentation and the manual delineation (for the LV ejection fraction and the LV myocardial mass, the correlation coefficients R are 0.9683 and 0.9278, respectively).We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.