Abstract: Ejection fraction of left ventricle is regarded as an important metric to measure the status of heart.To enhance the accuracy of left ventricle segmentation and ejection fraction estimation,the paper presents a novel framework which bases on improved fully convolutional networks(FCN)and fully connected conditional random field(fc CRF).Firstly,the framework segmented the region of left ventricle from MRI using a pre-trained FCN and obtained probability maps.Secondly,post-processing of pixel-wise label assignment was performed by 3D fc CRF.Finally,the segmentation was reconstructed in 3D;end-systolic volume and end-diastolic volume were acquired,and ejection fraction of left ventricle were then calculated.The results demonstrate the framework can estimate the left ventricular ejection fraction accurately and efficiently;the mean predicted error of left ventricular ejection fraction is 4.67% and the time-consuming is short.