Evidence map›Paper›PMID 40425199›Full record

ArticleBMJ open ophthalmology2025

Application of generative adversarial networks in the restoration of blurred optical coherence tomography images caused by optical media opacity in eyes.

Zhengfang Wang, Shuang Zhou, Yeye Zhang, Jianwei Lin, Jinyan Lin, Ming Zhu, Tsz Kin Ng, Weifeng Yang, Geng Wang

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Article in BMJ open ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Zhengfang Wang *Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China.
Shuang Zhou *School of Physics and Optoelectronic Engineering, Hainan University, Haikou, China.
Yeye ZhangSchool of Physics and Optoelectronic Engineering, Hainan University, Haikou, China.
Jianwei LinJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China.
Jinyan LinDepartment of Physics, Shantou University, Shantou, China.
Ming ZhuSchool of Physics and Optoelectronic Engineering, Hainan University, Haikou, China.
Tsz Kin NgJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China.
Weifeng YangSchool of Physics and Optoelectronic Engineering, Hainan University, Haikou, China.
Geng WangJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China wg@jsiec.org.ORCID 0000-0001-8476-1955

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess the application of generative adversarial networks (GANs) to restore the blurred optical coherence tomography (OCT) images caused by optical media opacity in eyes.

methodsIn this cross-sectional study, a spectral-domain OCT (Zeiss Cirrus 5000, Germany) was used to scan the macula of 510 eyes from 272 Chinese subjects. Optical media opacity was simulated with an algorithm for training set (420 normal eyes). Images for three test sets were from the following: 56 normal eyes before and after fitting neutral density filter (NDF), 34 eyes before and after cataract surgeries and 90 eyes processed by algorithm. GANs of pix2pix was trained with training set and restored blurred images in test sets. Structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were used to evaluate the performance of GANs.

resultsPSNR for test sets before and after image restoration was 18.37±0.44 and 19.94±0.29 for NDF (p＜0.01), 16.65±0.99 and 16.91±0.26 for cataract (p=0.68) and 18.33±0.55 and 20.83±0.41 for algorithm regenerated graph (p＜0.01), respectively. SSIM for test sets before and after image restoration was 0.85±0.02 and 1.00±0.00 for NDF (p＜0.01), 0.92±0.07 and 0.97±0.02 for cataract (p＜0.01) and 0.86±0.02 and 0.99±0.01 for algorithm regenerated graph (p＜0.01), respectively.

conclusionsGANs can be used to restore blurred OCT images caused by optical media opacity in eyes. Future studies are warranted to optimise this technique before the application in clinical practice.

Indexed as

CataractMacula LuteaNeural Networks, ComputerTomography, Optical CoherenceAdultAgedAlgorithmsCataract ExtractionCross-Sectional StudiesFemaleGenerative Adversarial NetworksHumansMaleMiddle AgedSignal-To-Noise RatioCataractImaging

Identifiers

PMID40425199
PMCPMC12107585

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.