基于全卷积网络的左心室射血分数自动检测

提出了一种基于全卷积网络(fully convolutional networks， FCN)的左心室射血分数自动估测的方法.利用全卷积网络对心脏磁共振图像中的左心室进行内轮廓分割，计算心脏左心室在一个心动周期中各时间点的容积，提取左心室舒张末期与收缩末期的容积，最后推导出左心室的射血分数.使用700组图片对全卷积网络进行训练以及440组图片进行测试，并将最后计算结果与美国国立卫生研究院和儿童国家医疗中心提供的射血分数(ejection fraction， EF)金标准进行了对比，计算准确率为89.8%，结果处在合理的误差范围内.

Automatic Detection of Left Ventricular Ejection Fraction Based on Fully Convolutional Networks

An automatic estimation method of left ventricular ejection fraction based on fully convolutional networks(FCN)is proposed. The left ventricle in magnetic resonance images(MRI) of heart is segmented using FCN. Furthermore， the volume of left ventricle can be calculated at each phase in a heart beat cycle. Finally， the volume of end-systolic and end-diastolic are extracted respectively to deduce the left ventricular ejection fraction. 700 sets of images are used for training the networks and 400 sets for testing. The final results agree well with the ejection fraction(EF)gold standard provided by the American National Institutes of Health and Children′s National Medical Center. The accuracy of the proposed method achieves 89.8%， which is within an acceptable range.