1. Articles from Jiang Liu

    1-24 of 36 1 2 »
    1. Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning

      Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning

      Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular ...

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    2. Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using AS-OCT images

      Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using AS-OCT images

      Nuclear cataract (NC) is a leading eye disease for blindness and vision impairment globally. Accurate and objective NC grading/classification is essential for clinically early intervention and cataract surgery planning. Anterior segment optical coherence tomography (AS-OCT) images are capable of capturing the nucleus region clearly and measuring the opacity of NC quantitatively. Recently, clinical research has suggested that the opacity correlation and repeatability between NC severity levels and the average nucleus density on AS-OCT images is high with the interclass and intraclass analysis. Moreover, clinical research has suggested that opacity distribution is uneven on the nucleus region, indicating that the ...

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    3. Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images

      Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images

      Nuclear cataract (NC) is a leading ocular disease globally for blindness and vision impairment. NC patients can improve their vision through cataract surgery or slow the opacity development with early intervention. Anterior segment optical coherence tomography (AS-OCT) image is an emerging ophthalmic image type, which can clearly observe the whole lens structure. Recently, clinicians have been increasingly studying the correlation between NC severity levels and clinical features from the nucleus region on AS-OCT images, and the results suggested the correlation is strong. However, automatic NC classification research based on AS-OCT images has rarely been studied. This paper presents a novel ...

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    4. Multiview Volume and Temporal Difference Network for Angle-Closure Glaucoma Screening from AS-OCT Videos

      Multiview Volume and Temporal Difference Network for Angle-Closure Glaucoma Screening from AS-OCT Videos

      Background. Precise and comprehensive characterizations from anterior segment optical coherence tomography (AS-OCT) are of great importance in facilitating the diagnosis of angle-closure glaucoma. Existing automated analysis methods focus on analyzing structural properties identified from the single AS-OCT image, which is limited to comprehensively representing the status of the anterior chamber angle (ACA). Dynamic iris changes are evidenced as a risk factor in primary angle-closure glaucoma. Method. In this work, we focus on detecting the ACA status from AS-OCT videos, which are captured in a dark-bright-dark changing environment. We first propose a multiview volume and temporal difference network (MT-net). Our method ...

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    5. Adaptive feature squeeze network for nuclear cataract classification in AS-OCT image

      Adaptive feature squeeze network for nuclear cataract classification in AS-OCT image

      Nuclear cataract (NC) is an age-related cataract disease. Cataract surgery is an effective method to improve the vision and life quality of NC patients. Anterior segment optical coherence tomography (AS-OCT) images are noninvasive, reproductive, and easy-measured, which can capture opacity clearly on the lens nucleus region. However, automatic AS-OCT-based NC classification research has not been extensively studied. This paper proposes a novel convolutional neural network (CNN) framework named Adaptive Feature Squeeze Network (AFSNet) to classify NC severity levels automatically. In the AFSNet, we construct an adaptive feature squeeze module to dynamically squeeze local feature representations and update the relative importance ...

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    6. Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images

      Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images

      Aims: To apply a deep learning model for automatic localisation of the scleral spur (SS) in anterior segment optical coherence tomography (AS-OCT) images and compare the reproducibility of anterior chamber angle (ACA) width between deep learning located SS (DLLSS) and manually plotted SS (MPSS). Methods: In this multicentre, cross-sectional study, a test dataset comprising 5166 AS-OCT images from 287 eyes (116 healthy eyes with open angles and 171 eyes with primary angle-closure disease (PACD)) of 287 subjects were recruited from four ophthalmology clinics. Each eye was imaged twice by a swept-source AS-OCT (CASIA2, Tomey, Nagoya, Japan) in the same visit ...

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    7. 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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    8. Angle-closure assessment in anterior segment OCT images via deep learning

      Angle-closure assessment in anterior segment OCT images via deep learning

      Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients' eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be ...

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    9. 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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    10. 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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    11. AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a ...

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    12. 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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    13. 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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    14. AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually leads to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a ...

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    15. 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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    16. 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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    17. Speckle reduction in optical coherence tomography images

      Speckle reduction in optical coherence tomography images

      An optical coherence tomography (OCT) image composed of a plurality of A-scans of a structure is analyzed by defining, for each A-scan, a set of neighboring A-scans surrounding the A-slices scan. Following an optional de-noising step, the neighboring A-scans are aligned in the imaging direction, then a matrix X is formed from the aligned A-scans, and matrix completion is performed to obtain a reduced speckle noise image.

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    18. 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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    19. Automatic fibroatheroma identification in intravascular optical coherence tomography volumes

      Automatic fibroatheroma identification in intravascular optical coherence tomography volumes

      Coronary heart disease is the most common type of heart disease that leads to heart attacks. The identification of vulnerable plaques, especially the thin-cap fibroatheroma (TCFA), is crucial to the diagnosis of coronary artery disease. Intravascular optical coherence tomography (IVOCT), an emerging imaging modality, has been proven to be useful for the identification of vulnerable plaques. In this work, we propose an approach to identify the volumes with fibroatheroma frames automatically. In the proposed method, we first detect the lumen using a graph-search based method from unfolded images. Then a region of interest starting from the lumen boundary is cropped ...

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    20. 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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    21. 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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    22. 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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    23. A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

      A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

      Purpose Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for presence of angle closure. Design Development of an artificial intelligence automated detection system for the presence of angle closure. Methods A deep learning system for automated angle-closure detection in AS-OCT images was developed, and this was compared with another automated angle-closure detection system based on quantitative features. A total of 4135 Visante AS-OCT images from 2113 subjects (8270 anterior chamber angle (ACA) images with 7375 open-angle and 895 angle-closure) were ...

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    24. Reducing speckle noise in optical coherence tomography images

      Reducing speckle noise in optical coherence tomography images

      A method and system are proposed to obtain a reduced speckle noise image of a subject from optical coherence tomography (OCT) image data of the subject. The cross sectional images each comprise a plurality of scan lines obtained by measuring the time delay of light reflected, in a depth direction, from optical interfaces within the subject. The method comprises two aligning steps. First the cross sectional images are aligned, then image patches of the aligned cross sectional images are aligned to form a set of aligned patches. An image matrix is then formed from the aligned patches; and matrix completion ...

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    1-24 of 36 1 2 »
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