基于卷积神经网络的左心室超声图像特征点定位  

Feature point localization of left ventricular ultrasound image based on convolutional neural network

作　　者：周玉金 王晓东[1] 张力戈 朱锴[1,2] 姚宇 ZHOU Yujin;WANG Xiaodong;ZHANG Lige;ZHU Kai;YAO Yu(Chengdu Institute of Computer Application,Chinese Academy of Sciences,Chengdu Sichuan 610041,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院成都计算机应用研究所,成都610041 [2]中国科学院大学,北京100049

出　　处：《计算机应用》2019年第4期1201-1207,共7页journal of Computer Applications

基　　金：四川省科技厅重点研发项目(2017SZ0010);四川省科技支撑计划项目(2016JZ0035)~~

摘　　要：针对传统级联卷积神经网络(CNN)在左心室超声图像中定位准确度较低的问题,提出一种融合更快速区域卷积神经网络(Faster-RCNN)模型提取区域的级联卷积神经网络,实现对超声图像中左心室心内膜和心外膜轮廓特征点的定位。首先,采用两级级联的方式改进传统级联卷积神经网络的网络结构,第一级网络利用一个改进的卷积网络粗略定位左心室心内膜和心外膜联合的特征点,第二级网络使用四个改进的卷积网络分别对心内膜特征点和心外膜特征点进行位置微调,之后定位输出左心室心内膜和心外膜联合的轮廓特征点位置;然后,将改进的级联卷积神经网络与目标区域提取融合,即利用Faster-RCNN模型提取包含左心室的目标区域并将目标区域送入改进的级联卷积神经网络;最后,由粗到细对左心室轮廓特征点进行定位。实验结果表明,与传统级联卷积神经网络相比,所提方法在左心室超声图像上的定位效果更好,更逼近真实值,在均方根误差的评价标准下,特征点定位准确度提升了32.6个百分点。In order to solve the problem that the traditional cascaded Convolutional Neural Network(CNN)has low accuracy of feature point localization in left ventricular ultrasound image,an improved cascaded CNN with region extracted by Faster Region-based CNN(Faster-RCNN)model was proposed to locate the left ventricular endocardial and epicardial feature points in ultrasound images.Firstly,the traditional cascaded CNN was improved by a structure of two-stage cascaded.In the first stage,an improved convolutional network was used to roughly locate the endocardial and epicardial joint feature points.In the second stage,four improved convolutional networks were used to fine-tune the endocardial feature points and the epicardial feature points separately.After that,the positions of joint contour feature points were output.Secondly,the improved cascaded CNN was merged with target region extraction,which means that the target region containing the left ventricle was extracted by the Faster-RCNN model and then was sent into the improved cascaded CNN.Finally,the left ventricular contour feature points were located from coarse to fine.Experimental results show that compared with the traditional cascaded CNN,the proposed method is much more accurate in left ventricle feature point localization,and its prediction points are closer to the actual values.Under the root mean square error evaluation standard,the accuracy of feature point localization is improved by 32.6 percentage points.

关 键 词：超声心动图 左心室 特征点定位 卷积神经网络 级联卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]