Abstract: To avoid the over-fitting phenomenon， a segmentation method of left ventricle endocardium based on transfer learning of FCN was proposed. The VGG network which had been trained through the natural images was fine-tuned. In addition， some segmentation criteria were employed to optimizing the results based on the priori information that the left ventricle endocardium was in the center of the MRI(magnetic resonance imaging). In the end， 45 cases taken from the 2009 MICCAI dataset was tested by this mothod. The computed DICE index， APD and GC ratio were 0.91， 1.73mm and 97.81%， respectively. Better results in segmentation of left ventricle endocardium were achieved through the transfer learning of fully convolutional networks and the priori information can improve the automatic segmentation results.

Key words: segmentation of left ventricle endocardium, deep learning, FCN(full convolutional networks), transfer learning, MRI(magnetic resonance imaging)