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


METHODS
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.


RESULTS
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).


CONCLUSIONS
We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
Yurun
Y
Ma
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
Li
L
Wang
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China. yidema14@126.com.
Yide
Y
Ma
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
Min
M
Dong
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
Shiqiang
S
Du
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
Xiaoguang
X
Sun
D000465:Algorithms; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D016277:Ventricular Function, Left
A priori constraints; Cardiac MRI; Gradient vector flow (GVF); Left ventricle segmentation; Simplified pulse-coupled neural network (SPCNN)