OCT Signal Enhancement with Deep Learning

Lazaridis, Georgios; Lorenzi, Marco; Mohamed-Noriega, Jibran; Aguilar-Munoa, Soledad; Suzuki, Katsuyoshi; Nomoto, Hiroki; Ourselin, Sébastien; Garway-Heath, David; Investigators, United Kingdom Glaucoma Treatment Study

doi:10.1016/j.ogla.2020.10.008

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…Recently, generative adversarial networks (GANs) 9 have been extensively used in the artificial intelligence community for the synthesis of artificial images and have emerged as a potential technique to address the challenge of data scarcity in biomedical applications. 10 In ophthalmic studies, GAN models have been demonstrated in segmentation, [11][12][13][14] data augmentation, [15][16][17][18][19] domain transfer, [20][21][22] image enhancement, [23][24][25][26] and others. 27 In data augmentation with GANs, prior studies on synthetic fundus 15,28,29 and optical coherence tomography (OCT) image generation 16,30 have largely focused on retinal disorders, which are assessed by the presence of lesions.…”

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confidence: 99%

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma

et al. 2022

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IMPORTANCE Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection.OBJECTIVE To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection.DESIGN, SETTING, AND PARTICIPANTS Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively.MAIN OUTCOMES AND MEASURES Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications.RESULTS A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images.CONCLUSIONS AND RELEVANCE DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.

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confidence: 99%

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma

et al. 2022

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“…The fundus autofluorescence images were generated from en-face OCT images using GAN to identify the geographic atrophy region Tavakkoli et al [ 62 ] Conditional GAN Fundus photography → Fluorescein angiography The proposed GAN produced anatomically accurate fluorescein angiography images that were indistinguishable from real angiograms Yoo et al [ 63 ] CycleGAN Ultra-widefield fundus photography → Fundus photography Ultra-widefield images were successfully translated into traditional fundus photography-style images by CycleGAN, and the main structural information of the retina and optic nerve was retained Ju et al [ 64 ] CycleGAN Fundus photography → Ultra-widefield fundus photography The CycleGAN model transferred the color fundus photographs to ultra-widefield images to introduce additional data for existing limited ultra-widefield images. The proposed method was adopted for diabetic retinopathy grading and lesion detection Lazaridis et al [ 91 , 108 ] Wasserstein GAN + perceptual loss (conditional GAN) Time-domain OCT → spectral-domain OCT Time-domain OCT was converted to synthetic spectral-domain OCT using GAN. The model improved the statistical power of the measurements when compared with those derived from the original OCT GAN = generative adversarial network; OCT = optical coherence tomography …”

Section: Reviewmentioning

confidence: 99%

Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

et al. 2022

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Background Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. Methods We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. Results In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. Conclusions The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.

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“…For image segmentation, GANs have been used to enhance retinal vessel [166][167][168] and corneal nerves segmentation [169]. GANs have also been applied to perform denoizing, enhancement and super-resolution in ophthalmic imaging, on OCT [170][171][172][173][174][175], fundus photography [176][177], and dehazing of cataractous retinal images [178]. In sum, GAN-driven image synthesis can greatly boost DL-based detection and diagnosis of diseases.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Lim

Hong²,

Lam

et al. 2022

Current Opinion in Ophthalmology

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Purpose of reviewThe application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. Recent findingsSeveral key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SummaryAI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

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OCT Signal Enhancement with Deep Learning

Cited by 14 publications

References 29 publications

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma

Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

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