基于深度学习的医学计算机辅助检测方法研究  

Medical computer-aided detection method based on deep learning

作　　者：陶攀[1,2] 付忠良 朱锴[1,2] 王莉莉[1,2] TAO Pan;FU Zhongliang;ZHU Kai;WANG Lili(Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China;University of Chinese Academy of Sciences, Beijing 100049, P.R.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：《生物医学工程学杂志》2018年第3期368-375,共8页Journal of Biomedical Engineering

基　　金：四川省科技支撑计划基金项目(2016JZ0035)

摘　　要：针对自动检测医学图像中指定目标时存在的问题,提出了一种基于深度学习自动检测目标位置和估计对象姿态的算法。该算法基于区域深度卷积神经网络和目标结构的先验知识,采用区域生成候选框网络、感兴趣区域池化策略,引入包括分类损失、边框位置回归定位损失和像平面内朝向损失的多任务损失函数,近似优化一个端到端的有监督定位网络,能快速地对医学图像中目标自动定位,有效地为下一步的分割和参数自动提取提供定位结果。并在超声心动图左心室检测中提出利用检测额外标记点(二尖瓣环、心内膜垫和心尖),能高效地对左心室朝向姿态进行估计。为了验证算法的鲁棒性和有效性,实验数据选取经食管超声心动图和核磁共振图像。实验结果表明算法是快速、精确和有效的。This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function： classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

关 键 词：计算机辅助检测 核磁共振图像 超声心动图 物体检测 区域卷积神经网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.72[自动化与计算机技术—控制科学与工程]