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    1. Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membranein 3D OCT Images Using Deep Convolutional Approaches

      Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membranein 3D OCT Images Using Deep Convolutional Approaches

      Epiretinal Membrane (ERM) is a disease caused by a thin layer of scar tissue that is formed on the surface of the retina. When this membrane appears over the macula, it can cause distorted or blurred vision. Although normally idiopathic, its presence can also be indicative of other pathologies such as diabetic macular edema or vitreous haemorrhage. ERM removal surgery can preserve more visual acuity the earlier it is performed. For this purpose, we present a fully automatic segmentation system that can help the clinicians to determine the ERM presence and location over the eye fundus using 3D Optical Coherence ...

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    2. DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

      DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

      Objective: Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automated, complete DR classification frameworks based on both OCT and OCTA data have not been proposed. In this study, a convolutional neural network (CNN) based method is proposed to fulfill a DR classification framework using en face OCT and OCTA.

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    3. Embedded Residual Recurrent Network and Graph Search for the Segmentation of Retinal Layer Boundaries in Optical Coherence Tomography

      Embedded Residual Recurrent Network and Graph Search for the Segmentation of Retinal Layer Boundaries in Optical Coherence Tomography

      For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer is essential for assessing the severity of the disease and diagnosing the progression of the disease. Optical coherence tomography (OCT) images can clearly show each layer of the retinal structure and detect subtle early lesions, thus providing a gold standard for the diagnosis of retinal diseases. In this article, we propose a coarse-to-fine retinal layer boundary segmentation method based on the embedded residual recurrent network (ERR-Net) and the graph search (GS). Considering the integrity of information transmission and the dependence of features, we first design ...

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    4. Inspection of Intraocular Lens with Dual-side View Optical Coherence Tomography

      Inspection of Intraocular Lens with Dual-side View Optical Coherence Tomography

      Intraocular lens (IOL) is widely used for cataract treatment. Its optical properties are crucial to obtain a good treatment efficacy and thus need to be evaluated and controlled. In this study, we propose a novel method based on optical coherence tomography (OCT) for noncontact and accurate in vitro measurement of thickness, refractive index and dioptric power of IOL implants. The OCT setup is specially designed to create two sampling optics, called dual-side view OCT (DSV-OCT), which allows for imaging IOL from the two opposite sides simultaneously in single OCT volume scanning. This can produce a three-dimensional surface contour without suffering ...

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    5. 1.7-micron Optical Coherence Tomography Angiography for Characterization of Skin Lesions – A Feasibility Study

      1.7-micron Optical Coherence Tomography Angiography for Characterization of Skin Lesions – A Feasibility Study

      Optical coherence tomography (OCT) is a non-invasive diagnostic method that offers real-time visualization of the layered architecture of the skin in vivo. The 1.7-micron OCT system has been applied in cardiology, gynecology and dermatology, demonstrating an improved penetration depth in contrast to conventional 1.3-micron OCT. To further extend the capability, we developed a 1.7-micron OCT/OCT angiography (OCTA) system that allows for a visualization of both morphology and microvasculature in the deeper layers of the skin. Using this imaging system, we imaged human skin with different benign lesions and described the corresponding features of both structure and ...

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      Mentions: UC Irvine
    6. Joint Segmentation and Quantification of Chorioretinal Biomarkers in Optical Coherence Tomography Scans: A Deep Learning Approach

      Joint Segmentation and Quantification of Chorioretinal Biomarkers in Optical Coherence Tomography Scans: A Deep Learning Approach

      In ophthalmology, chorioretinal biomarkers (CRBMs) play a significant role in detecting, quantifying, and ameliorating the treatment of chronic eye conditions. Optical coherence tomography (OCT) imaging is primarily used for investigating various CRBMs and prompt intervention of retinal conditions. However, with extensive clinical applications and increasing prevalence of ocular diseases, the number of OCT scans obtained globally exceeds ophthalmologists’ capacity to examine these in a meaningful manner. Instead, the emergence of deep learning provides a cost-effective and reliable alternative for automated analysis of scans, assisting ophthalmologists in clinical routines and research. This paper presents a residual learning-based framework (RASP-Net) that integrates ...

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    7. Deep Relation Transformer for Diagnosing Glaucoma with Optical Coherence Tomography and Visual Field Function

      Deep Relation Transformer for Diagnosing Glaucoma with Optical Coherence Tomography and Visual Field Function

      Glaucoma is the leading reason for irreversible blindness. Early detection and timely treatment of glaucoma are essential for preventing visual field loss or even blindness. In clinical practice, Optical Coherence Tomography (OCT) and Visual Field (VF) exams are two widely-used and complementary techniques for diagnosing glaucoma. OCT provides quantitative measurements of the optic nerve head (ONH) structure, while VF test is the functional assessment of peripheral vision. In this paper, we propose a Deep Relation Transformer (DRT) to perform glaucoma diagnosis with OCT and VF information combined. A novel deep reasoning mechanism is proposed to explore implicit pairwise relations between ...

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    8. Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

      Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

      Manual assessment of the retinal thickness in optical coherence tomography images is a time-consuming task, prone to error and inter-observer variability. The wide variability of the retinal appearance makes the automation of retinal image processing a challenging problem to tackle. The difficulty is even more accentuated in practice when the retinal tissue exhibits large structural changes due to disruptive pathology. In this work, we propose an ensemble-learning-based method for the automated segmentation of retinal boundaries in optical coherence tomography images that is robust to retinal abnormalities. The segmentation accuracy of the proposed algorithm was evaluated on two publicly available datasets ...

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    9. Super-Achromatic Rapid Scanning Microendoscope for Ultrahigh-Resolution OCT Imaging

      Super-Achromatic Rapid Scanning Microendoscope for Ultrahigh-Resolution OCT Imaging

      Microendoscope is a critical technology to enable high-resolution imaging of internal luminal organs with optical coherence tomography. This paper reports the development of an achromatic compound microlens and a rapid scanning microendoscope based on the microlens that offers an ultrahigh transverse resolution of 4 mum (and an axial resolution of 2.2 mum when using a low-coherence light source with a broad spectrum bandwidth of 150 nm). The rapid scanning endoscope is capable of ultrahigh-resolution (UHR) optical coherence tomography (OCT) imaging in real time at an imaging speed of about 1220 lateral scans/s, with the image quality comparable to ...

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    10. 4-D Imaging of Beating Tissues using Optical Coherence Tomography

      4-D Imaging of Beating Tissues using Optical Coherence Tomography

      An imaging technique which reconstructs structure and flow in tissue with repetitive motion was developed using Optical Coherence Tomography (OCT). The demonstrated technique is able to accurately image both host tissue and flow at different time points during a cyclic motion, such as a cardiac cycle. Using tissue-mimicking phantoms, a phase-sensitive spectral-domain OCT system was combined with a cyclic-motion simulator to demonstrate the feasibility of the method. 3-D flow information at different time points in the cyclical motion was reconstructed in the temporal domain thus generating the 4-D imaging of the target tissue. This method could expand OCT-based studies such ...

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    11. A Lightweight Mimic Convolutional Auto-Encoder for Denoising Retinal Optical Coherence Tomography Images

      A Lightweight Mimic Convolutional Auto-Encoder for Denoising Retinal Optical Coherence Tomography Images

      Optical coherence tomography (OCT) is widely used for diagnosing and monitoring retinal disorders. However, despite hardware improvements, its scans are still highly affected by speckle noise. Speckle noise reduces quality of measurements and decreases reliability of further instrumentation. Recent OCT denoising methods are often complex and computationally inefficient, despite their valid performance. These methods can be used as reference methods to train deep auto-encoders (AEs). AE networks can learn important structural features of OCT images that have been denoised with these reference methods and use features to reconstruct or denoise corrupted ones. In this way, a well-trained AE can efficiently ...

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    12. Ultrahigh-speed spectral domain optical coherence tomography up to 1 MHz A-scan rate using space-time division multiplexing

      Ultrahigh-speed spectral domain optical coherence tomography up to 1 MHz A-scan rate using space-time division multiplexing

      The primary optimization of the imaging speed of optical coherence tomography (OCT) has been keenly studied. In order to overcome the major speed limitation of spectral-domain OCT (SD-OCT), we developed an ultrahigh speed SD-OCT system, with an A-scan rate of up to 1 MHz, using the method of space-time division multiplexing (STDM). Multi-cameras comprising a single spectrometer was implemented in the developed ultrahigh-speed STDM method to eliminate the dead time of operation, whereas space-time division multiplexing was simultaneously employed to enable wide-range scanning measurements at a high speed. By successfully integrating the developed STDM method with GPU parallel processing, 8 ...

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    13. Speckle Noise Reduction for OCT Images based on Image Style Transfer and Conditional GAN

      Speckle Noise Reduction for OCT Images based on Image Style Transfer and Conditional GAN

      Raw optical coherence tomography (OCT) images typically are of low quality because speckle noise blurs retinal structures, severely compromising visual quality and degrading performances of subsequent image analysis tasks. In our previous study, we have developed a Conditional Generative Adversarial Network (cGAN) for speckle noise removal in OCT images collected by several commercial OCT scanners, which we collectively refer to as scanner T. In this paper, we improve the cGAN model and apply it to our in-house OCT scanner (scanner B) for speckle noise suppression. The proposed model consists of two steps: 1) We train a Cycle-Consistent GAN (CycleGAN) to ...

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    14. Portable Optical Coherence Elastography System With Flexible and Phase Stable Common Path Optical Fiber Probe

      Portable Optical Coherence Elastography System With Flexible and Phase Stable Common Path Optical Fiber Probe

      Biomechanical properties drive the functioning of cells and tissue. Measurement of such properties in the clinic is quite challenging, however. Optical coherence elastography is an emerging technique in this field that can measure the biomechanical properties of the tissue. Unfortunately, such systems have been limited to benchtop configuration with limited clinical applications. A truly portable system with a flexible probe that could probe different sample sites with ease is still missing. In this work, we report a portable optical coherence elastography system based on a flexible common path optical fiber probe. The common path approach allows us to reduce the ...

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    15. Modeling of Retinal Optical Coherence Tomography Based on Stochastic Differential Equations: Application to Denoising

      Modeling of Retinal Optical Coherence Tomography Based on Stochastic Differential Equations: Application to Denoising

      In this paper a statistical modeling, based on stochastic differential equations (SDEs), is proposed for retinal Optical Coherence Tomography (OCT) images. In this method, pixel intensities of image are considered as discrete realizations of a Levy stable process. This process has independent increments and can be expressed as response of SDE to a white symmetric alpha stable (sαs) noise. Based on this assumption, applying appropriate differential operator makes intensities statistically independent. Mentioned white stable noise can be regenerated by applying fractional Laplacian operator to image intensities. In this way, we modeled OCT images as sαs distribution. We applied ...

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    16. Learning With Fewer Images via Image Clustering: Application to Intravascular OCT Image Segmentation

      Learning With Fewer Images via Image Clustering: Application to Intravascular OCT Image Segmentation

      Deep learning based methods are routinely used to segment various structures of interest in varied medical imaging modalities. Acquiring annotations for a large number of images requires a skilled analyst, and the process is both time consuming and challenging. Our approach to reduce effort is to reduce the number of images needing detailed annotation. For intravascular optical coherence tomography (IVOCT) image pullbacks, we tested 10% to 100% of training images derived from two schemes: equally-spaced image subsampling and deep-learning- based image clustering. The first strategy involves selecting images at equally spaced intervals from the volume, accounting for the high spatial ...

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    17. Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach

      Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach

      Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted ...

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    18. Boundary Aware U-Net for Retinal Layers Segmentation in Optical Coherence Tomography Images

      Boundary Aware U-Net for Retinal Layers Segmentation in Optical Coherence Tomography Images

      Retinal layers segmentation in optical coherence tomography (OCT) images is a critical step in the diagnosis of numerous ocular diseases. Automatic layers segmentation requires separating each individual layer instance with accurate boundary detection, but remains a challenging task since it suffers from speckle noise, intensity inhomogeneity, and the low contrast around boundary. In this work, we proposed a boundary aware U-Net (BAU-Net) for retinal layers segmentation by detecting accurate boundary. Based on encoder-decoder architecture, we design a dual tasks framework with low-level outputs for boundary detection and high-level outputs for layers segmentation by using different supervisions. Specifically, we first use ...

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    19. Discrimination of diabetic retinopathy from optical coherence tomography angiography images using machine learning methods

      Discrimination of diabetic retinopathy from optical coherence tomography angiography images using machine learning methods

      Purpose: The goal was to discriminate between diabetic retinopathy (DR) and healthy controls (HC) by evaluating Optical coherence tomography angiography (OCTA) images from 3×3 mm scans with the assistance of different machine learning models. Methods: The OCTA angiography dataset of superficial vascular plexus (SVP), deep vascular plexus (DVP), and retinal vascular network (RVN) were acquired from 19 DR (38 eyes) patients and 25 HC (44 eyes). A discrete wavelet transform was applied to extract texture features from each image. Four machine learning models, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), support vector machine ...

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    20. Rupture detection during needle insertion using complex OCT data and CNNs

      Rupture detection during needle insertion using complex OCT data and CNNs

      Objective: Soft tissue deformation and ruptures complicate needle placement. However, ruptures at tissue inter- faces also contain information which helps physicians to navigate through different layers. This navigation task can be challenging, whenever ultrasound (US) image guidance is hard to align and externally sensed forces are superimposed by friction. Methods: We propose an experimental setup for reproducible needle insertions, applying optical coherence tomography (OCT) directly at the needle tip as well as external US and force measurements. Processing the complex OCT data is challenging as the penetration depth is limited and the data can be difficult to interpret. Using a ...

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    21. On-Chip Beam Splitting Strategies Based on SWG Assisted Directional Coupler for 850 nm Optical Coherence Tomography - A Numerical Study

      On-Chip Beam Splitting Strategies Based on SWG Assisted Directional Coupler for 850 nm Optical Coherence Tomography - A Numerical Study

      A subwavelength gratings (SWG) assisted directional coupler (DC) is proposed to work as beam splitter (BS) for 850 nm chip-based optical coherence tomography (OCT). The bandwidth of the SWG assisted DC is maximized to 110 nm by selecting appropriate SWG geometries including its duty cycle and period. The axial point spread functions (PSFs) derived from the BS transmittances proves the optimized bandwidth is broad enough to support axial resolution higher than 4 μ m . Meanwhile, the SWG period can be manipulated to match the center wavelength between BS and light source so that the wavelength mismatching induced resolution degradation is effectively ...

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    22. Inpainting for Saturation Artifacts in Optical Coherence Tomography using Dictionary-based Sparse Representation

      Inpainting for Saturation Artifacts in Optical Coherence Tomography using Dictionary-based Sparse Representation

      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 ...

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    23. Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography

      Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography

      Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation ...

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    24. Confidence-guided Topology-preserving Layer Segmentation for Optical Coherence Tomography ...

      Confidence-guided Topology-preserving Layer Segmentation for Optical Coherence Tomography ...

      Optical coherence tomography (OCT) imaging widely used in retinal examinations yields high resolution cross-sectional scans of the retina. As a key indicator for studying the development of retinopathy, the change of layer thickness needs to be accurately measured. Although many deep learning-based segmentation methods have been developed, most of them do not explicitly consider the strict order of the retina layers, which easily leads to topological errors. In this paper, we propose a novel segmentation framework that employs the distance maps of layer surfaces to convert the segmentation task into multitasking problem for classification and regression, and obtains the topology-guaranteed ...

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