基于全卷积网络迁移学习的左心室内膜分割

为了避免过拟合现象，提出了基于全卷积网络迁移学习的左心室内膜分割方法.该方法在已用自然图像训练好的VGGNet模型的基础上对参数进行微调;其次，利用了心室内膜位于MRI图像中心处的先验信息作为选取准则来优化分割结果.将该方法对2009 MICCAI数据集的45个病例进行测试，其DICE指数、APD距离和GC率分别为0.91，1.73mm和97.81%.测试结果表明该方法对于心脏MRI图像的左心室内膜的分割结果较好，当引入一定的先验信息后可以优化测试结果.

Segmentation of Left Ventricle Endocardium Based on Transfer Learning of Fully Convolutional Networks

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.