生成对抗网络对OCT视网膜图像的超分辨率重建  

Super-Resolution Reconstruction of Optical Coherence Tomography Retinal Images by Generating Adversarial Network

作　　者：柯舒婷 陈明惠[1] 郑泽希 袁媛[1] 王腾 何龙喜 吕林杰 孙好 Ke Shuting;Chen Minghui;Zheng Zexi;Yuan Yuan;Wang Teng;He Longxi;Lü Linjie;Sun Hao(Shanghai Engineering Research Center of Interventional Medical Device,the Ministry of Education of Medical Optical Engineering Center,School of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学健康科学与工程学院,上海介入医疗器械工程技术研究中心,教育部医学光学工程中心,上海200093 [2]上海理工大学机械工程学院,上海200093

出　　处：《中国激光》2022年第15期84-92,共9页Chinese Journal of Lasers

基　　金：上海市科委产学研医项目(15DZ1940400)。

摘　　要：光学相干层析成像(OCT)的质量通常会受到固有散斑噪声和低采样率的影响。为了在短扫描时间内获得高信噪比和高分辨率的OCT图像,本文提出了一种改进的OCT图像超分辨率重建网络模型PPECA-SRGAN。该模型将生成对抗网络(GAN)作为基础结构,可以不依赖配对数据集进行训练。在该模型的生成器残差块之间添加了金字塔注意力模块PANet,同时在判别器中加入了本文新提出的PECA模块,使其更加注重捕捉图像细节,提升模型对图像边缘纹理的重建能力。将所提PPECA-SRGAN模型在OCT图像数据集上进行实验,得到的峰值信噪比和结构相似性指标的平均值较当前三种经典模型的平均值分别约提高了3.5%和5.6%。实验结果表明,所提模型在鲁棒性和OCT图像细节重建方面较经典模型有较大提升。Objective Optical coherence tomography(OCT)imaging shows great potential in clinical practice because of its n oninvasive nature.However,two critical issues affect the diagnostic capability of OCT imaging.The first problem is that the interferential nature of OCT imaging produces interference noise,which reduces contrast and obfuscates fine structural features.The second problem is caused by the low spatial sampling rate of OCT.In fact,in clinical diagnosis,the use of a lower spatial sampling rate is a method to achieve a wide field of vision and reduce the impact of unconscious movement.Therefore,most OCT images obtained in reality are not optimal in terms of signal-to-noise ratio and spatial sampling rate.There are significant differences in the texture and brightness of the retinal layer in patients,as well as in the shape and size of the lesion area,so traditional models may not be able to reliably reconstruct the pathological structure.To obtain high peak signal-to-noise ratio(PSNR)and high-resolution B-scan OCT images,it is necessary to develop sufficient methods for super-resolution reconstruction of OCT images.In this paper,an improved OCT superresolution image reconstruction network structure(PPECA-SRGAN)was proposed.Methods In this paper,a PPECA-SRGAN network based on generative adversarial network(GAN)was proposed.The n etwork model includes a generator and a discriminator.A PA module was added between the residual blocks of the generator to increase the feature extraction capability of OCT retinal image reconstruction.In addition,a PECA module was added to the discriminator,which is an improvement of the pyramid split attention network(PSANet)and can fully capture the spatial information of multi-scale feature maps.First,we used two data sets to test a training set of 1000 images and a test set of 50 images,respectively.The data set was imported into the preprocessing module,and the lowresolution image was obtained through four down-sampling processes.Then,the generator was used to train the model to

关 键 词：生物光学 光学相干层析成像 超分辨率 生成对抗网络 无配对图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]