A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera  

作　　者：Junxia Fu Lvchen Cao Shihui Wei Ming Xub Yali Song Huiqi Li Yuxia You 

机构地区：[1]Beijing Aier Intech Eye Hospital,Beijing,China [2]Aier Eye Hospital Group,Hunan,China [3]Department of Ophthalmology,The Chinese People's Liberation Army General Hospital,Beijing,China [4]School of Artificial Intelligence,Henan University,Zhengzhou,China [5]School of Information and Electronics,Beijing Institute of Technology,Beijing,China

出　　处：《Advances in Ophthalmology Practice and Research》2022年第3期38-46,共9页眼科实践与研究新进展（英文）

摘　　要：Objective:Due to limited imaging conditions,the quality of fundus images is often unsatisfactory,especially for images photographed by handheld fundus cameras.Here,we have developed an automated method based on combining two mirror-symmetric generative adversarial networks(GANs)for image enhancement.Methods:A total of 1047 retinal images were included.The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment.All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists.The quality classification and quality change of images were compared.In addition,imagedetailed reading results for the number of dubiously pathological fundi were also compared.Results:After GAN enhancement,42.9% of images increased their quality,37.5%remained stable,and 19.6%decreased.After excluding the images at the highest level(level 0)before enhancement,a large number(75.6%)of images showed an increase in quality classification,and only a minority(9.3%)showed a decrease.The GANenhanced method was superior for quality improvement over a luminosity and contrast adjustment method(P<0.001).In terms of image reading results,the consistency rate fluctuated from 86.6%to 95.6%,and for the specific disease subtypes,both discrepancy number and discrepancy rate were less than 15 and 15%,for two ophthalmologists.Conclusions:Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.

关 键 词：Generative adversarial networks(GANs) Retinal image QUALITY Handheld fundus cameras 

分 类 号：R774.1[医药卫生—眼科]