基于金字塔长程Transformer的OCT图像超分辨率重建  

Super⁃Resolution Reconstruction of OCT Image Based on Pyramid Long⁃Range Transformer

作　　者：芦焱琦 陈明惠[1] 秦楷博 吴玉全 尹志杰 杨政奇 Lu Yanqi;Chen Minghui;Qin Kaibo;Wu Yuquan;Yang Zhengqi(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)

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

出　　处：《中国激光》2023年第15期61-72,共12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)在眼科方面的应用通常受到散斑噪声和低分辨率的影响.目前主流的OCT图像超分辨率重建方法多基于卷积神经网络,往往存在成像质量低、图像过度平滑和边缘细节缺失等情况.本文提出了基于Transformer的OCT视网膜图像超分辨率网络——TESR.TESR加入了边缘增强模块,以加强边缘信息对模型的影响,提高视网膜各层边缘的清晰度;新提出的金字塔长程Transformer模块融合了局部特征和全局表示,对图像的内部信息进行长程建模,能更有效地学习更丰富的图像特征.实验结果表示:本文所提TESR模型在峰值信噪比和结构相似度这两个指标上比其他经典模型均有一定程度的提高,在学习感知图像块相似度这一指标上表现优秀,同时在主观视觉质量上也有明显提高,泛化能力较强.Objective Optical coherence tomography(OCT)is widely employed for ophthalmic imaging and diagnosis because of its low latency,noncontact nature,noninvasiveness,high resolution,and high sensitivity.However,two major issues have hindered the development of OCT diagnostics for ophthalmology.First,OCT images are inevitably corrupted by scattering noise owing to the lowcoherence interferometric imaging modality,which severely degrades the quality of OCT images.Second,low sampling rates are often used to accelerate the acquisition process and reduce the impact of unconscious motion in clinical practice.This practice leads to a reduction in the resolution of OCT images.With the development of deep learning,the use of neural networks to achieve superresolution reconstruction of OCT images has compensated for the shortcomings of traditional methods and has gradually become mainstream.Most current mainstream super-resolution OCT image reconstruction networks adopt convolutional neural networks,which mainly use local feature extraction to recover low-resolution OCT images.However,traditional models based on convolutional neural networks typically encounter two fundamental problems that originate from the underlying convolutional layers.First,the interaction between the image and convolutional kernel is content-independent,and second,using the same convolutional kernel to recover different image regions may not be the best choice.This often leads to problems,such as excessive image smoothing,missing edge structures,and failure to reliably reconstruct pathological structures.In addition,acquiring real OCT images affects the training effectiveness of previous models.First,deep learning models usually require a large amount of training data to avoid overfitting;however,it is difficult to obtain a large number of real OCT images.Second,even if the results are excellent,it is meaningless to train the model without using images acquired from OCT devices commonly used in today’s clinics.To address the above problems,this study pr

关 键 词：医用光学 光学相干断层成像 超分辨率 TRANSFORMER 自注意力 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]