Cardiac functional analysis is important in heart disease diagnosis. Conventional manual segmentation of left ventricle is time consuming and observer dependent. Our proposed Densely Connected Full Convolutional Network (DenseV-Net) enables automatically process medical images. Its densely connected convolutional block consists of residual calculation with Elu used as active function. The results show that the proposed DenseV-Net can efficiently segment left ventricle from cardiac cines with mean DSC of 0.90±0.12, more accurate compared to V-Net (0.85±0.13, P<0.05). The method offers a feasible way for efficient analysis of cardiac function.