Segmentation of the left ventricle(LV) using the distance regularized level set evolution(DRLSE) model causes it to be jagged and poorly segmented. To solve these problems currently faced by LV segmentation, this paper firstly uses a convolutional neural network (CNN)-based myocardial center-line detection algorithm to replace the manual initialization process of the level set method, and secondly proposes a non-zero level set-based preserving convexity LV segmentation method. Comparing the mean degree centrality of the DRLSE (level set method), deep learning method and the new method, it is found that the DC (dice coefficient) of the new method at the end-systole (ES) is 0.93, which is higher than the other methods. In addition, the mean Hausdorff distance (HD) of the new method at the end-diastolic (ED) and ES phases are 2.51 and 2.54, respectively, which is significantly smaller than those of the deep learning method and the level set method. The experimental results show that the new method can effectively improve the segmentation accuracy.