【摘要】 左心室射血分数是临床上用于衡量心脏健康的一项重要指标.为提高左心室分割和射血分数计算的精度,提出一种基于改进的全卷积神经网络和全连接条件随机场的方法.首先利用预训练的全卷积神经网络模型对心脏核磁共振影像进行左心室分割并输出概率图;之后采用3D全连接条件随机场对概率图进行后处理,完成像素级的精准密度预测;最后对左心室分割结果进行3D重建,并计算左心室舒张末期容积和收缩末期容积,进而计算出射血分数.实验结果表明,该方法能够实现左心室射血分数的精确且高效的计算,对左心室射血分数的平均预测误差为4.67%,各步骤耗时短.更多还原

【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 3 D fc CRF. Finally, the segmentation was reconstructed in 3 D; 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.更多还原