Abstract:Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing … Show more
“…The main obstacle to accurate evaluation of the thickness is precise outlining of the corneal tissue boundaries. Besides the manual segmentation or semi-automated traditional methods (Chen et al, 2012;Corre-Perez et al, 2012;Li et al, 2012;Xu et al, 2016;Ang et al, 2018;Morishige et al, 2019;Yang et al, 2020;Toprak et al, 2021), deep learning-based methods have been proposed for corneal tissue interface segmentation (Mathai et al, 2019;Ouyang et al, 2019;Santos et al, 2019). In our previous studies, we compared these methods with our proposed hierarchyconstrained segmentation network (Liu et al, 2020) and validated the effectiveness of our network architecture and boundary constraint.…”
Section: Discussionmentioning
confidence: 95%
“…Whereas manual labeling is time-consuming and has poor repeatability, the traditional image processing methods are less robust to deal with pathological corneas (Larocca et al, 2011;Williams et al, 2015;Ang et al, 2018;Elsawy et al, 2019). Recent studies have explored the feasibility of using deep learning-based methods for corneal tissue segmentation with AS-OCT images (Mathai et al, 2019;Ouyang et al, 2019;Santos et al, 2019). We have also proposed a hierarchy-constrained network, which robustly improves the segmentation performance of the corneal tissue interfaces in both normal and KC eyes (Liu et al, 2020).…”
PurposeTo characterize the corneal and epithelial thickness at different stages of keratoconus (KC), using a deep learning based corneal segmentation algorithm for anterior segment optical coherence tomography (AS-OCT).MethodsAn AS-OCT dataset was constructed in this study with 1,430 images from 715 eyes, which included 118 normal eyes, 134 mild KC, 239 moderate KC, 153 severe KC, and 71 scarring KC. A deep learning based corneal segmentation algorithm was applied to isolate the epithelial and corneal tissues from the background. Based on the segmentation results, the thickness of epithelial and corneal tissues was automatically measured in the center 6 mm area. One-way ANOVA and linear regression were performed in 20 equally divided zones to explore the trend of the thickness changes at different locations with the KC progression. The 95% confidence intervals (CI) of epithelial thickness and corneal thickness in a specific zone were calculated to reveal the difference of thickness distribution among different groups.ResultsOur data showed that the deep learning based corneal segmentation algorithm can achieve accurate tissue segmentation and the error range of measured thickness was less than 4 μm between our method and the results from clinical experts, which is approximately one image pixel. Statistical analyses revealed significant corneal thickness differences in all the divided zones (P < 0.05). The entire corneal thickness grew gradually thinner with the progression of the KC, and their trends were more pronounced around the pupil center with a slight shift toward the temporal and inferior side. Especially the epithelial thicknesses were thinner gradually from a normal eye to severe KC. Due to the formation of the corneal scarring, epithelial thickness had irregular fluctuations in the scarring KC.ConclusionOur study demonstrates that our deep learning method based on AS-OCT images could accurately delineate the corneal tissues and further successfully characterize the epithelial and corneal thickness changes at different stages of the KC progression.
“…The main obstacle to accurate evaluation of the thickness is precise outlining of the corneal tissue boundaries. Besides the manual segmentation or semi-automated traditional methods (Chen et al, 2012;Corre-Perez et al, 2012;Li et al, 2012;Xu et al, 2016;Ang et al, 2018;Morishige et al, 2019;Yang et al, 2020;Toprak et al, 2021), deep learning-based methods have been proposed for corneal tissue interface segmentation (Mathai et al, 2019;Ouyang et al, 2019;Santos et al, 2019). In our previous studies, we compared these methods with our proposed hierarchyconstrained segmentation network (Liu et al, 2020) and validated the effectiveness of our network architecture and boundary constraint.…”
Section: Discussionmentioning
confidence: 95%
“…Whereas manual labeling is time-consuming and has poor repeatability, the traditional image processing methods are less robust to deal with pathological corneas (Larocca et al, 2011;Williams et al, 2015;Ang et al, 2018;Elsawy et al, 2019). Recent studies have explored the feasibility of using deep learning-based methods for corneal tissue segmentation with AS-OCT images (Mathai et al, 2019;Ouyang et al, 2019;Santos et al, 2019). We have also proposed a hierarchy-constrained network, which robustly improves the segmentation performance of the corneal tissue interfaces in both normal and KC eyes (Liu et al, 2020).…”
PurposeTo characterize the corneal and epithelial thickness at different stages of keratoconus (KC), using a deep learning based corneal segmentation algorithm for anterior segment optical coherence tomography (AS-OCT).MethodsAn AS-OCT dataset was constructed in this study with 1,430 images from 715 eyes, which included 118 normal eyes, 134 mild KC, 239 moderate KC, 153 severe KC, and 71 scarring KC. A deep learning based corneal segmentation algorithm was applied to isolate the epithelial and corneal tissues from the background. Based on the segmentation results, the thickness of epithelial and corneal tissues was automatically measured in the center 6 mm area. One-way ANOVA and linear regression were performed in 20 equally divided zones to explore the trend of the thickness changes at different locations with the KC progression. The 95% confidence intervals (CI) of epithelial thickness and corneal thickness in a specific zone were calculated to reveal the difference of thickness distribution among different groups.ResultsOur data showed that the deep learning based corneal segmentation algorithm can achieve accurate tissue segmentation and the error range of measured thickness was less than 4 μm between our method and the results from clinical experts, which is approximately one image pixel. Statistical analyses revealed significant corneal thickness differences in all the divided zones (P < 0.05). The entire corneal thickness grew gradually thinner with the progression of the KC, and their trends were more pronounced around the pupil center with a slight shift toward the temporal and inferior side. Especially the epithelial thicknesses were thinner gradually from a normal eye to severe KC. Due to the formation of the corneal scarring, epithelial thickness had irregular fluctuations in the scarring KC.ConclusionOur study demonstrates that our deep learning method based on AS-OCT images could accurately delineate the corneal tissues and further successfully characterize the epithelial and corneal thickness changes at different stages of the KC progression.
“…An important application of this work is to better the accuracy of segmentation. In clinic diagnosis, accurate estimation of tissue boundaries is crucial to determine the quantitative parameter for treatment [ 24 ]. With the existence of saturation pixels, the pixel information needed to estimate boundaries is blurry.…”
Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler’s elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.
“…The method improved the image quality metrics for OCT Mahapatra et al [ 55 ] PGGAN with a conditional design Fundus photography Super-resolution Image super-resolution using multi-stage PGGAN outperforms competing methods and baseline GANs. The super-resolved images can be used for landmark and pathology detection Huang et al [ 49 ] Conditional GAN Retinal OCT Super-resolution and removing noise The GAN model effectively suppressed speckle noise and super-resolved OCT images at different scales Ouyang et al [ 51 ] Conditional GAN Anterior Segment OCT Removing speckle noise The model removed undesired specular artifacts and speckle-noise patterns to improve the visualization of corneal and limbal OCT images Yoo et al [ 53 ] CycleGAN Fundus photography Removing artifacts and noise The GAN model removed the artifacts automatically in a fundus photograph without matching paired images Cheong et al [ 16 ] DeshadowGAN (modified conditional GAN with perceptual loss) Peripapillary retinal OCT (spectral domain) Removing vessel shadow artifacts The GAN model using manually masked artifact images and perceptual loss function removed blood vessel shadow artifacts from OCT images of the optic nerve head Chen et al [ 50 ] Conditional GAN Peripapillary retinal OCT (spectral domain) Removing speckle noise The GAN model was designed for speckle noise reduction in OCT images and preserved the textural details found in OCT Das et al [ 52 ] CycleGAN Retinal OCT Super-resolution and removing noise To achieve denoising and super-resolution, adversarial learning with cycle consistency was used without requiring aligned low–high resolution pairs …”
Section: Reviewmentioning
confidence: 99%
“…Cheong et al built DeShadowGAN using manually masked artifact images and conditional GAN with perceptual loss and demonstrated the effectiveness of the model in removing shadow artifacts [ 16 ]. Similarly, conditional GAN has also been applied to remove speckle noise in peripapillary retinal OCT [ 50 ] and anterior segment OCT [ 51 ]. However, image denoising methods using conditional GAN can match low- and high-quality image pairs; however, these data are typically unavailable in the medical field.…”
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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