Accurate Estimation of Left Ventricle Ejection Fraction Using Fully Convolutional Networks and Fully Connected Conditional Random Field

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.