1. Articles from Jiang Liu

    1-24 of 30 1 2 »
    1. 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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    2. 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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    3. 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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    4. 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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    5. 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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    6. 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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    7. 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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    8. 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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    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. 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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    12. 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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    13. 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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    14. 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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    15. 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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    16. 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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    17. 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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    18. 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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    19. Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT

      Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT

      Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected ...

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    20. Methods and systems for characterizing angle closure glaucoma for risk assessment or screening

      Methods and systems for characterizing angle closure glaucoma for risk assessment or screening

      A method is proposed for analyzing an optical coherence tomography (OCT) image of the anterior segment (AS) of a subject's eye. A region of interest is defined which is a region of the image containing the junction of the cornea and iris, and an estimated position the junction within the region of interest is derived. Using this a second region of the image is obtained, which is a part of the image containing the estimated position of the junction. Features of the second region are obtained, and those features are input to an adaptive model to generate data characterizing ...

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    21. Axial Alignment for Anterior Segment Swept Source Optical Coherence Tomography via Robust Low-Rank Tensor Recovery

      Axial Alignment for Anterior Segment Swept Source Optical Coherence Tomography via Robust Low-Rank Tensor Recovery

      We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously, this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information, the proposed method is more robust compared to using only individual or paired slices, and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes, the preliminary results indicate that the proposed axial alignment method can not only facilitate manual ...

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    22. Speckle Reduction in 3D Optical Coherence Tomography of Retina by A-Scan Reconstruction

      Speckle Reduction in 3D Optical Coherence Tomography of Retina by A-Scan Reconstruction

      Optical coherence tomography (OCT) is a micrometer-scale, cross-sectional imaging modality for biological tissue. It has been widely used for retinal imaging in ophthalmology. Speckle noise is problematic in OCT. A raw OCT image/volume usually has very poor image quality due to speckle noise, which often obscures the retinal structures. Overlapping scan is often used for speckle reduction in a 2D line-scan. However, it leads to an increase of the data acquisition time. Therefore, it is unpractical in 3D scan as it requires a much longer data acquisition time. In this paper, we propose a new method for speckle reduction ...

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    23. Corneal graft evaluation based on optical coherence tomography image

      Corneal graft evaluation based on optical coherence tomography image

      An OCT image of an eye which has been subject to a DSAEK corneal transplant, in which a Descement's membrane in the cornea has been replaced by a graft, is processed to identify the outline of the graft. The process includes the steps of: computationally extracting the boundary of the cornea including the graft; computationally detecting the corners of the graft; computationally extracting points on the boundary between the graft and the original cornea; and computationally fitting the points on the boundary between the graft and the original cornea smoothly into a curve. The outline of the graft is ...

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    24. Speckle reduction in optical coherence tomography by matrix completion using bilateral random projection

      Speckle reduction in optical coherence tomography by matrix completion using bilateral random projection

      Speckle noise is problematic in optical coherence tomography (OCT) and often obscures the structure details. In this paper, we propose a new method to reduce speckle noise from multiply scanned OCT slices. The proposed method registers the OCT scans using a global alignment followed by a local alignment based on global and local motion estimation. Then low rank matrix completion using bilateral random projection is utilized to estimate the noise and recover the clean image. Experimental results show that the proposed method archives average contrast to noise ratio 14.90, better than 13.78 by the state-of-the-art method used in ...

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    1-24 of 30 1 2 »
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