1. Articles from Jianlong Yang

    1-21 of 21
    1. Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset

      Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset

      Purpose: To develop and test semi-supervised generative adversarial networks (GANs) that detect retinal disorders on optical coherence tomography (OCT) images using a small-labeled dataset. Methods: From a public database, we randomly chose a small supervised dataset with 400 OCT images (100 choroidal neovascularization, 100 diabetic macular edema, 100 drusen, and 100 normal) and assigned all other OCT images to unsupervised dataset (107,912 images without labeling). We adopted a semi-supervised GAN and a supervised deep learning (DL) model for automatically detecting retinal disorders from OCT images. The performance of the 2 models was compared in 3 testing datasets with different ...

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    2. Noise reduction by adaptive-SIN filtering for retinal OCT images

      Noise reduction by adaptive-SIN filtering for retinal OCT images

      Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and directional sensitivity. In the paper, we propose an adaptive denoising algorithm for OCT images. The OCT noise is closer to the Poisson distribution than the Gaussian distribution, and shearlet transform assumes additive white Gaussian noise. We hence propose a square-root transform to redistribute the OCT noise. Different manufacturers and differences between imaging objects may influence the observed noise characteristics, which make predefined thresholding scheme ineffective ...

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    3. Semi-supervised generative adversarial networks for closed-angle detection on anterior segment optical coherence tomography images: an empirical study with a small training dataset

      Semi-supervised generative adversarial networks for closed-angle detection on anterior segment optical coherence tomography images: an empirical study with a small training dataset

      Background: Semi-supervised learning algorithms can leverage an unlabeled dataset when labeling is limited or expensive to obtain. In the current study, we developed and evaluated a semi-supervised generative adversarial networks (GANs) model that detects closed-angle on anterior segment optical coherence tomography (AS-OCT) images using a small labeled dataset. Methods: In this cross-sectional study, a semi-supervised GANs model was developed for automatic closed-angle detection training on a small labeled and large unsupervised training dataset collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong (JSIEC). The closed-angle was defined as iris-trabecular contact beyond ...

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    4. Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection

      Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection

      Purpose : To develop generative adversarial networks (GANs) that synthesize realistic anterior segment optical coherence tomography (AS-OCT) images and evaluate deep learning (DL) models that are trained on real and synthetic datasets for detecting angle closure. Methods : The GAN architecture was adopted and trained on the dataset with AS-OCT images collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, synthesizing open- and closed-angle AS-OCT images. A visual Turing test with two glaucoma specialists was performed to assess the image quality of real and synthetic images. DL models, trained on either real or ...

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    5. ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

      ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

      Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE ...

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    6. Open-Appositional-Synechial Anterior Chamber Angle Classification in AS-OCT Sequences

      Open-Appositional-Synechial Anterior Chamber Angle Classification in AS-OCT Sequences

      Anterior chamber angle (ACA) classification is a key step in the diagnosis of angle-closure glaucoma in Anterior Segment Optical Coherence Tomography (AS-OCT). Existing automated analysis methods focus on a binary classification system (i.e., open angle or angle-closure) in a 2D AS-OCT slice. However, clinical diagnosis requires a more discriminating ACA three-class system (i.e., open, appositional, or synechial angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types. To address this, we propose a novel sequence multi-scale aggregation deep network (SMA-Net) for open-appositional-synechial ACA classification based on an AS-OCT ...

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    7. Assessment of Generative Adversarial Networks Model for Synthetic Optical Coherence Tomography Images of Retinal Disorders

      Assessment of Generative Adversarial Networks Model for Synthetic Optical Coherence Tomography Images of Retinal Disorders

      Purpose : To assess whether a generative adversarial network (GAN) could synthesize realistic optical coherence tomography (OCT) images that satisfactorily serve as the educational images for retinal specialists, and the training datasets for the classification of various retinal disorders using deep learning (DL). Methods : The GANs architecture was adopted to synthesize high-resolution OCT images trained on a publicly available OCT dataset, including urgent referrals (37,206 OCT images from eyes with choroidal neovascularization, and 11,349 OCT images from eyes with diabetic macular edema) and nonurgent referrals (8617 OCT images from eyes with drusen, and 51,140 OCT images from normal ...

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    8. Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation

      Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation

      Optical coherence tomography (OCT) is a rapidly developing non-invasive three dimensional imaging approach, and it has been widely used in examination and diagnosis of eye diseases. However, speckle noise are often inherited from image acquisition process, and may obscure the anatomical structure, such as the retinal layers. In this paper, we propose a novel method to reduce the speckle noise in 3D OCT scans, by introducing a new super-resolution approach. It uses a multi-frame fusion mechanism that merges multiple scans for the same scene, and utilizes the movements of sub-pixels to recover missing signals in one pixel, which significantly improves ...

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    9. Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning

      Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning

      The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo, because it does not suffer from the information contamination of the outer retina in fundus photography and scanning laser ophthalmoscopy and the resolution deficiency in ocular ultrasound. We propose a biomarker infused global-to-local network, for the choroid segmentation. It leverages the thickness of the choroid layer, which is a primary biomarker in clinic, as a constraint to improve the segmentation accuracy. We also design a global-to-local ...

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    10. BIONET: INFUSING BIOMARKER PRIOR INTO GLOBAL-TO-LOCAL NETWORK FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES

      BIONET: INFUSING BIOMARKER PRIOR INTO GLOBAL-TO-LOCAL NETWORK FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES

      Choroid is the vascular layer of the eye, which is directly related to the incidence and severity of many ocular diseases. Optical Coherence Tomography (OCT) is capable of imaging both the cross-sectional view of retina and choroid, but the segmentation of the choroid region is challenging because of the fuzzy choroid-sclera interface (CSI). In this paper, we propose a biomarker infused global-to-local network (BioNet) for choroid segmentation, which segments the choroid with higher credibility and robustness. Firstly, our method trains a biomarker prediction network to learn the features of the biomarker. Then a global multi-layers segmentation module is applied to ...

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    11. PERCEPTUAL-ASSISTED ADVERSARIAL ADAPTATION FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY

      PERCEPTUAL-ASSISTED ADVERSARIAL ADAPTATION FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY

      Accurate choroid segmentation in optical coherence tomography (OCT) image is vital because the choroid thickness is a major quantitative biomarker of many ocular diseases. Deep learning has shown its superiority in the segmentation of the choroid region but subjects to the performance degeneration caused by the domain discrepancies (e.g., noise level and distribution) among datasets obtained from the OCT devices of different manufacturers. In this paper, we present an unsupervised perceptual-assisted adversarial adaptation (PAAA) framework for efficiently segmenting the choroid area by narrowing the domain discrepancies between different domains. The adversarial adaptation module in the proposed framework encourages the ...

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    12. High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

      High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

      Reducing the bit-depth is an effective approach to lower the cost of optical coherence tomography (OCT) systems and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit-depth will lead to the degeneration of the detection sensitivity thus reduce the signal-to-noise ratio (SNR) of OCT images. In this paper, we propose to use deep learning for the reconstruction of the high SNR OCT images from the low bit-depth acquisition. Its feasibility was preliminarily evaluated by applying the proposed method to the quantized 3 ∼ 8-bit data from native 12-bit interference fringes. We employed a pixel-to-pixel generative adversarial network ...

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    13. Resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning

      Resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning

      Optical coherence tomography angiography (OCTA) requires high transverse sampling rates for visualizing retinal and choroidal capillaries, which impedes the popularization of the OCTA technique due to the high cost of speedy acquisition systems. On the other hand, current wide-field OCTA using low transverse sampling causes the underestimation of vascular biomarkers in quantitative analysis. In this paper, we propose to use deep learning to repair the resolution degeneration induced by the low transverse sampling. We conducted preliminary experiments on converting the centrally cropped 3 × 3 mm2 field of view (FOV) of the 8 × 8 mm2 foveal OCTA images (a sampling rate ...

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    14. Upside-down position leads to choroidal expansion and anterior chamber shallowing: OCT study

      Upside-down position leads to choroidal expansion and anterior chamber shallowing: OCT study

      Background To determine whether dynamic changes in choroidal thickness (CT) cause shallowing of the anterior chamber. Methods 34 healthy volunteers were enrolled. The participants in our study adopted the upside-down position for 1.5 min, which was the model we used to study the dynamic changes in CT. Intraocular pressure (IOP) elevation, optical coherence tomography images of the choroid and anterior chamber were obtained at baseline, after being in an upside-down position in an inversion machine and after 15 min of rest. The changes in IOP, anterior chamber and choroidal blood flow between the baseline and the upside-down position were ...

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    15. Optical Coherence Tomography Angiography and UltraWidefield Optical Coherence Tomography in a Child With Incontinentia Pigmenti

      Optical Coherence Tomography Angiography and UltraWidefield Optical Coherence Tomography in a Child With Incontinentia Pigmenti

      Incontinentia pigmenti (IP) is a rare X-linked dominant disorder that can cause retinal nonperfusion, neovascularization, and retinal detachment. Evaluation of the peripheral retinal vasculature and appropriate treatment can reduce the risk of blindness. The authors report the use of a handheld prototype optical coherence tomography angiography (OCTA) and ultra-widefield OCT (UWF-OCT) during exam under anesthesia of a 2-year-old with a history of severe early onset IP. UWF-OCT and OCTA may be used as noninvasive imaging modalities for IP and similar retinal vascular disorders in supine young children.

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    16. Optical Coherence Tomography Angiography and Ultra-Widefield Optical Coherence Tomography in a Child With Incontinentia Pigmenti

      Optical Coherence Tomography Angiography and Ultra-Widefield Optical Coherence Tomography in a Child With Incontinentia Pigmenti

      Incontinentia pigmenti (IP) is a rare X-linked dominant disorder that can cause retinal nonperfusion, neovascularization, and retinal detachment. Evaluation of the peripheral retinal vasculature and appropriate treatment can reduce the risk of blindness. The authors report the use of a handheld prototype optical coherence tomography angiography (OCTA) and ultra-widefield OCT (UWF-OCT) during exam under anesthesia of a 2-year-old with a history of severe early onset IP. UWF-OCT and OCTA may be used as noninvasive imaging modalities for IP and similar retinal vascular disorders in supine young children.

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    17. Polarization-multiplexed, dual-beam swept source optical coherence tomography angiography

      Polarization-multiplexed, dual-beam swept source optical coherence tomography angiography

      A polarization-multiplexed, dual-beam setup is proposed to expand the field of view for a swept source optical coherence tomography angiography (OCTA) system. This method used a Wollaston prism to split sample path light into two orthogonal-polarized beams. This allowed two beams to shine on the cornea at an angle separation of ~ 14 degrees, which led to a separation of ~ 4.2 mm on the retina. A 3-mm glass plate was inserted into one of the beam paths to set a constant path length difference between the two polarized beams so the interferogram from the two beams are coded at different ...

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    18. Handheld OCT Angiography and Ultra–Wide-Field OCT in Retinopathy of Prematurity

      Handheld OCT Angiography and Ultra–Wide-Field OCT in Retinopathy of Prematurity

      Importance Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. Optical coherence tomography (OCT) has improved the care of adults with vitreoretinal disease, and OCT angiography (OCTA) is demonstrating promise as a technique to visualize the retinal vasculature with lower risk and cost than fluorescein angiography. However, to date, there are no commercially available devices able to obtain ultra–wide-field OCT or OCTA images in neonates. Objective To obtain ultra–wide-field OCT and OCTA images in neonates with ROP using a prototype handheld OCT and OCTA device. Design, Setting, and Participants This observational case series was conducted ...

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    19. Extended axial imaging range, widefield swept source optical coherence tomography angiography

      Extended axial imaging range, widefield swept source optical coherence tomography angiography

      We developed a high-speed, swept source OCT system for widefield OCT angiography (OCTA) imaging. The system has an extended axial imaging range of 6.6 mm. An electrical lens is used for fast, automatic focusing. The recently developed split-spectrum amplitude and phase-gradient angiography allow high-resolution OCTA imaging with only two B-scan repetitions. An improved post-processing algorithm effectively removed trigger jitter artifacts and reduced noise in the flow signal. We demonstrated high contrast 3 mm×3 mm OCTA image with 400×400 pixels acquired in 3 seconds and high-definition 8 mm×6 mm and 12 mm×6 mm OCTA images with ...

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    20. Handheld optical coherence tomography angiography

      Handheld optical coherence tomography angiography

      We developed a handheld optical coherence tomography angiography (OCTA) system using a 100-kHz swept-source laser. The handheld probe weighs 0.4 kg and measures 20.6 × 12.8 × 4.6 cm 3 . The system has dedicated features for handheld operation. The probe is equipped with a mini iris camera for easy alignment. Real-time display of the en face OCT and cross-sectional OCT images in the system allows accurately locating the imaging target. Fast automatic focusing was achieved by an electrically tunable lens controlled by a golden-section search algorithm. An extended axial imaging range of 6 mm allows easy alignment. A ...

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    21. Hematocrit dependence of flow signal in optical coherence tomography angiography

      Hematocrit dependence of flow signal in optical coherence tomography angiography

      The hematocrit dependence of flow signal (split-spectrum amplitude decorrelation angiography-SSADA decorrelation value) was investigated in this paper. Based on the normalized field temporal correlation function and concentration dependent particle scattering properties, the relationship between hematocrit and flow signal was analytically derived. Experimental verification of the relationship was performed with custom-designed microfluidic chips and human blood with 45%, 40% and 32% hematocrit. It was found that, in large flow channels and blood vessels, the normal hematocrit is near the decorrelation saturation point and therefore a change in hematocrit has little effect on the SSADA decorrelation value (flow signal). However, in narrow ...

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    1-21 of 21
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    Hematocrit dependence of flow signal in optical coherence tomography angiography Handheld optical coherence tomography angiography Extended axial imaging range, widefield swept source optical coherence tomography angiography Handheld OCT Angiography and Ultra–Wide-Field OCT in Retinopathy of Prematurity Polarization-multiplexed, dual-beam swept source optical coherence tomography angiography Optical Coherence Tomography Angiography and Ultra-Widefield Optical Coherence Tomography in a Child With Incontinentia Pigmenti Optical Coherence Tomography Angiography and UltraWidefield Optical Coherence Tomography in a Child With Incontinentia Pigmenti Resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning PERCEPTUAL-ASSISTED ADVERSARIAL ADAPTATION FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY The truth about invisible posterior vitreous structures Increased Macrophage-like Cell Density in Retinal Vein Occlusion as Characterized by en Face Optical Coherence Tomography