Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image  

作　　者：Yiwei Chen Yi He Hong Ye Lina Xing Xin Zhang Guohua Shi 

机构地区：[1]Jiangsu Key Laboratory of Medical Optics,Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou 215163,P.R.China [2]School of Biomedical Engineering(Suzhou),Division of Life Sciences and Medicine,University of Science and Technology of China Hefei 230026,P.R.China [3]Center for Excellence in Brain Science and Intelligence Technology,Chinese Academy of Sciences,Shanghai 200031,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2024年第3期105-113,共9页创新光学健康科学杂志（英文）

基　　金：supported in part by the Gusu Innovation and Entrepreneurship Leading Talents in Suzhou City,grant numbers ZXL2021425 and ZXL2022476;Doctor of Innovation and Entrepreneurship Program in Jiangsu Province,grant number JSSCBS20211440;Jiangsu Province Key R&D Program,grant number BE2019682;Natural Science Foundation of Jiangsu Province,grant number BK20200214;National Key R&D Program of China,grant number 2017YFB0403701;National Natural Science Foundation of China,grant numbers 61605210,61675226,and 62075235;Youth Innovation Promotion Association of Chinese Academy of Sciences,grant number 2019320;Frontier Science Research Project of the Chinese Academy of Sciences,grant number QYZDB-SSW-JSC03;Strategic Priority Research Program of the Chinese Academy of Sciences,grant number XDB02060000.

摘　　要：The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.

关 键 词：Fundus fluorescein angiography image fundus structure image image translation unified deep learning model generative adversarial networks 

分 类 号：R54[医药卫生—心血管疾病] TP391.41[医药卫生—内科学]