改进型循环生成对抗网络的血管内超声图像增强  

Improved CycleGANs for Intravascular Ultrasound Image Enhancement

作　　者：姚哲维 杨丰[1,2] 黄靖[1,2] 刘娅琴[1,2] YAO Zhe-wei;YANG Feng;HUANG Jing;LIU Ya-qin(School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Guangdong Provincial Key Laboratory of Medical Image Processing,Southern Medical University,Guangzhou 510515,China)

机构地区：[1]南方医科大学生物医学工程学院,广州510515 [2]南方医科大学广东省医学图像处理重点实验室,广州510515

出　　处：《计算机科学》2019年第5期221-227,共7页Computer Science

基　　金：国家自然科学基金(61771233)资助

摘　　要：血管内超声(Intravascular Ultrasound,IVUS)图像采集过程中使用的低频或高频超声探头各有其特点,医生在诊断冠脉粥样硬化等疾病过程中需根据临床需求选择频率不同的超声探头。因此,文中提出一种基于Wasserstein距离的循环生成对抗网络(Cycle Generative Adversarial Networks,CycleGANs)用于增强血管内超声图像,其目的是融合高频超声细节信号,克服低频超声图像边缘模糊且分辨率较低等问题,以辅助医生诊断心血管疾病。首先基于心血管的形状特点,使用旋转、缩放和Gamma变换等方法将图像数据训练集增加30倍,降低网络训练过拟合的风险;然后利用对抗网络训练的思想,构建基于对抗损失和循环一致损失的联合函数,在损失函数中引入Wasserstein距离作为正则项,加快网络训练的收敛速度,解决训练不稳定的问题;最后以低频IVUS图像为输入对象,输出含有高频图像细节信息的IVUS增强图像。在实验过程中以国际标准IVUS图像数据库为基础进行算法验证比较;以清晰度、对比度和边缘能量为评价标准进行定量分析。实验结果:所提算法的收敛速度是原始CycleGANs模型的两倍,且3个评价标准数值分别提升了15.8%,11.4%和46.6%。实验结果表明:W-CycleGANs模型能够有效地学习高频图像域的特征信息,并且进一步丰富低频图像边缘细节,增强图像的诊断信息,提高医生判断心血管疾病的敏感性。此外,文中采用100幅临床IVUS图像数据进行推广验证,也获得了较好的增强效果。Low-frequency and high-frequency ultrasound probes used in intravascular ultrasound(IVUS)image acquisition have their own characteristics.Doctors have to choose ultrasound probes with different frequencies according to clinical needs during the diagnosis of Coronary atherosclerosis and other diseases.Therefore,a Cycle Generative Adversarial Networks(CycleGANs)based on the Wasserstein distance for intravascular ultrasound images enhancement was presented to combine high-frequency ultrasonic details and overcome the problems of edge blur and low resolution of low-frequency ultrasound image,assisting doctors in the diagnosis of cardiovascular disease.Firstly,according to the shape characteristics of coronary artery,several approaches used for data augmentation such as rotating,scaling up or down and implementing gamma transformation,are applied to increase the number of IVUS samples in training set,in order to reduce the risk of over-fitting during the training stage.Then,in the spirit of adversarial training,a joint loss function based on adversarial loss and cycle-consistent loss is constructed.Finally,the Wasserstein distance is added to the loss function as a regular term to stabilize the training and accelerated the convergence process.The input of this model is a low-frequency IVUS image and the output is an enhanced IVUS image containing high frequency detail information.An international standard IVUS image database was used for verification in the experiment.Clarity,contrast and edge energy were used as evaluation criteria to quantify.It is verified that the convergence speed of this model is twice of the original CycleGANs model.Three evaluation criteria are increased by 15.8%,11.4%and 46.6%,respectively.The experimental results show that the W-CycleGANs model can learn the feature information of the image domain effectively.Based on the original CycleGANs algorithm,it can further enrich the details of image edges and enhance the diagnostic information,also improve the sensitivity of doctors to diagnosis ca

关 键 词：血管内超声 生成对抗网络 Wasserstein距离 图像增强 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]