1. 1-24 of 426 1 2 3 4 ... 16 17 18 »
    1. Multi-scale pathological fluid segmentation in OCT with a novel curvature loss in convolutional neural network

      Multi-scale pathological fluid segmentation in OCT with a novel curvature loss in convolutional neural network

      The segmentation of pathological fluid lesions in optical coherence tomography (OCT), including intraretinal fluid, subretinal fluid, and pigment epithelial detachment, is of great importance for the diagnosis and treatment of various eye diseases such as neovascular age-related macular degeneration and diabetic macular edema. Although significant progress has been achieved with the rapid development of fully convolutional neural networks (FCN) in recent years, some important issues remain unsolved. First, pathological fluid lesions in OCT show large variations in location, size, and shape, imposing challenges on the design of FCN architecture. Second, fluid lesions should be continuous regions without holes inside. But ...

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    2. Broadband wavelength swept laser with bidirectional photonic resonance for high resolution optical coherence tomography

      Broadband wavelength swept laser with bidirectional photonic resonance for high resolution optical coherence tomography

      A light source with extended spectral bandwidth applied in optical coherence tomography (OCT) systems produces highly accurate micro - scale imaging structures owing to the attainable high - axial resolution. This study presents a wide tuning range of a wavelength - swept laser to improve the axial resolution. Linear cavity configuration was performed for bidirectional photonic resonance using two semiconductor optical amplifiers placed parallelly. A polygon scanner - based tunable filter produced a broa d scanning range of 155 nm from 917 nm to 1072 nm and an axial resolution of 3.4 μm in air. Bench - top imaging of a phantom made of ...

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    3. A handheld fiber-optic probe to enable optical coherence tomography of oral soft tissue

      A handheld fiber-optic probe to enable optical coherence tomography of oral soft tissue

      This study presents a highly miniaturized, handheld probe developed for rapid assessment of soft tissue using optical coherence tomography (OCT). OCT is a non-invasive optical technology capable of visualizing the sub-surface structural changes that occur in soft tissue disease such as oral lichen planus. However, usage of OCT in the oral cavity has been limited, as the requirements for high-quality optical scanning have often resulted in probes that are heavy, unwieldy and clinically impractical. In this paper, we present a novel probe that combines an all-fiber optical design with a light-weight magnetic scanning mechanism to provide easy access to the ...

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    4. ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images

      ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in  Optical Coherence Tomography Images

      Understanding the changes in choroidal thickness and vasculature is important to monitor the development and progression of various ophthalmic diseases. Accurate segmentation of the choroid layer and choroidal vessels is critical to better analyze and understand the choroidal changes. In this study, we develop a dense dilated U-Net model (ChoroidNET) for segmenting the choroid layer and choroidal vessels in optical coherence tomography (OCT) images. The performance of ChoroidNET is evaluated using an OCT dataset that contains images with various retinal pathologies. Overall Dice coefficient of 95.1 ± 0.4 and 82.4 ± 2.4 were obtained for choroid layer and ...

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    5. A Machine Learning Based Quantitative Data Analysis for Screening Skin Abnormality Based on Optical Coherence Tomography Angiography (OCTA)

      A Machine Learning Based Quantitative Data Analysis for Screening Skin Abnormality Based on Optical Coherence Tomography Angiography (OCTA)

      Lack of accurate and standard quantitative evaluations limit the progress of applying the OCTA technique into skin clinical trials. More systematic research is required to investigate the possibility of using quantitative OCTA techniques for screening skin diseases. This prospective study included 88 participants (60 normal and 28 abnormal skin samples). In total, 40 OCTA quantitative parameters (3 for epidermis feature, 27 for dermis feature, 10 for vascular feature) were obtained by each OCT and OCTA data volumes. The proposed method relies on linear support vector machines (SVM), while the coefficient of multiple linear regression is also employed to select seven ...

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    6. Improved accuracy of tissue glucose measurement using low magnification optical coherence tomography

      Improved accuracy of tissue glucose measurement using low magnification optical coherence tomography

      Optical coherence tomography (OCT) has a comparatively high spatial resolution among tomographic bioimaging techniques and is less affected by changes in physiological conditions such as temperature, blood pressure, and osmolytes in the tissue. OCT detects changes in the refractive index of tissues, which is a function of the tissue glucose concentration (TGC). OCT signal intensity generally decreases with tissue depth, and its slope is expected to show a negative correlation with TGC in the interstitial fluid, reflecting blood glucose concentration (BGC). The currently applied OCT system for measuring TGC does not satisfy the accuracy for clinical demand, mainly because of ...

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    7. Single A-Line Method for Fast Sample-Structure-Nondependent Dispersion Compensation of FD-OCT

      Single A-Line Method for Fast Sample-Structure-Nondependent Dispersion Compensation of FD-OCT

      Here we proposed a single A-line sample-structure-nondependent (SSNd) dispersion detection and compensation method for Fourier-domain optical coherence tomography (FD-OCT), without need for acquiring and processing B-scan data. A new FD-OCT dispersion mismatch index, based on the line slope of the bright line in the A-line spectrogram, has been presented in the proposed method. With the new dispersion mismatch index, the proposed single A-line method can fast and visually detect the dispersion mismatch states of FD-OCT setup and perform the SSNd dispersion compensation, just using a single A-line. Experimental results of multiple samples demonstrated the advantages and convenience of the proposed ...

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    8. A Novel Network With Parallel Resolution Encoders for the Diagnosis of Corneal Diseases

      A Novel Network With Parallel Resolution Encoders for the Diagnosis of Corneal Diseases

      Objective: To propose a deep-learning network for the diagnosis of two corneal diseases: Fuchs’ endothlelial dystrophy and keratoconus, based on optical coherence tomography (OCT) images of the cornea. Methods: In this paper, we propose a novel network with parallel resolution-specific encoders and composite classification features to directly diagnose Fuchs’ endothelial dystrophy and keratoconus using OCT images. Our proposed network consists of a multi-resolution input, multiple parallel encoders, and a composite of convolutional and dense features for classification. The purpose of using parallel resolution-specific encoders is to perform multi-resolution feature fusion. Also, using composite classification features enhances the dense feature learning ...

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    9. LamNet: A Lesion Attention Maps-Guided Network for the Prediction of Choroidal Neovascularization Volume in SD-OCT Images

      LamNet: A Lesion Attention Maps-Guided Network for the Prediction of Choroidal Neovascularization Volume in SD-OCT Images

      Choroidal neovascularization (CNV) volume prediction has an important clinical significance to predict the therapeutic effect and schedule the follow-up. In this paper, we propose a Lesion Attention Maps-Guided Network (LamNet) to automatically predict the CNV volume of next follow-up visit after therapy based on 3-dimentional spectral-domain optical coherence tomography (SD-OCT) images. In particular, the backbone of LamNet is a 3D convolutional neural network (3D-CNN). In order to guide the network to focus on the local CNV lesion regions, we use CNV attention maps generated by an attention map generator to produce the multi-scale local context features. Then, the multi-scale of ...

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    10. Investigation of Micro-motion Kinematics of Continuum Robots for Volumetric OCT and OCT-guided Visual Servoing

      Investigation of Micro-motion Kinematics of Continuum Robots for Volumetric OCT and OCT-guided Visual Servoing

      Continuum robots (CR) have been recently shown capable of micron-scale motion resolutions. Such motions are achieved through equilibrium modulation using indirect actuation for altering either internal preload forces or changing the cross-sectional stiffness along the length of a continuum robot. Previously reported, but unexplained, turning point behavior is modeled using two approaches. An energy minimization approach is first used to explain the source of this behavior. Subsequently, a kinematic model using internal constraints in multi-backbone CRs is used to replicate this turning point behavior. An approach for modeling the micro-motion differential kinematics is presented using experimental data based on the ...

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    11. Semi-Automated Extraction of Lens Fragments Via a Surgical Robot Using Semantic Segmentation of OCT Images With Deep Learning - Experimental Results in Ex Vivo Animal Model

      Semi-Automated Extraction of Lens Fragments Via a Surgical Robot Using Semantic Segmentation of OCT Images With Deep Learning - Experimental Results in Ex Vivo Animal Model

      The overarching goal of this work is to demonstrate the feasibility of using optical coherence tomography (OCT) to guide a robotic system to extract lens fragments from ex vivo pig eyes. A convolutional neural network (CNN) was developed to semantically segment four intraocular structures (lens material, capsule, cornea, and iris) from OCT images. The neural network was trained on images from ten pig eyes, validated on images from eight different eyes, and tested on images from another ten eyes. This segmentation algorithm was incorporated into the Intraocular Robotic Interventional Surgical System (IRISS) to realize semi-automated detection and extraction of lens ...

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      Mentions: UCLA
    12. Aberration Mitigation in High-resolution Optical Coherence Tomography Implementing Elliptical Beam Design

      Aberration Mitigation in High-resolution Optical Coherence Tomography Implementing Elliptical Beam Design

      We report an elliptical beam design for aberration mitigation in high-resolution optical coherence tomography (OCT). We polished a large angle on the fiber terminal facet in the sample arm to make a non-rotational symmetric beam with different numerical apertures (NA) for the two axes vertical to the optical axis. By sacrificing the resolution in the out-of-plane transverse direction, the elliptical beam mitigated the aberration introduced by the focusing optics in the OCT system. The elliptical beam with a doubled NA in the in-plane transverse direction promoted the axial field-of-view (FOV) by about 50% and increased the signal back-coupling efficiency by ...

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    13. Indoor localization of hand-held OCT probe using visual odometry and real-time segmentation using deep learning

      Indoor localization of hand-held OCT probe using visual odometry and real-time segmentation using deep learning

      Objective: Optical coherence tomography (OCT) is an established medical imaging modality that has found widespread use due to its ability to visualize tissue structures at a high resolution. Currently, OCT hand-held imaging probes lack positional information, making it difficult or even impossible to link a specific image to the location it was originally obtained. In this study, we propose a camera-based localization method to track and record the scanner position in real-time, as well as providing a deep learning-based segmentation method. Methods: We used camera-based visual odometry (VO) and simultaneous mapping and localization (SLAM) to compute and visualize the location ...

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    14. Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images

      Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images

      Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by ...

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    15. Joint segmentation of multi-class hyper-reflective foci in retinal optical coherence tomography images

      Joint segmentation of multi-class hyper-reflective foci in retinal optical coherence tomography images

      Hyper-reflective foci (HRF) refers to the spot-shaped, block-shaped areas with characteristics of high local contrast and high reflectivity, which is mostly observed in retinal optical coherence tomography (OCT) images of patients with fundus diseases. HRF mainly appears hard exudates (HE) and microglia (MG) clinically. Accurate segmentation of HE and MG is essential to alleviate the harm in retinal diseases. However, it is still a challenge to segment HE and MG simultaneously due to similar pathological features, various shapes and location distribution, blurred boundaries, and small morphology dimensions. To tackle these problems, in this paper, we propose a novel global information ...

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    16. MultiSDGAN: translation of OCT images to superresolved segmentation labels using multi-discriminators in multi-stages

      MultiSDGAN: translation of OCT images to superresolved segmentation labels using multi-discriminators in multi-stages

      Optical coherence tomography (OCT) has been identified as a non-invasive and inexpensive imaging modality to discover potential biomarkers for Alzheimer's diagnosis and progress determination. Current hypotheses presume the thickness of the retinal layers, which are analyzable within OCT scans, as an effective biomarker for the presence of Alzheimer's. As a logical first step, this work concentrates on the accurate segmentation of retinal layers to isolate the layers for further analysis. This paper proposes a generative adversarial network (GAN) that concurrently learns to increase the image resolution for higher clarity and then segment the retinal layers. We propose a ...

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    17. Retinal OCT Image Registration: Methods and Applications

      Retinal OCT Image Registration: Methods and Applications

      Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in evaluating longitudinal disease progression, reducing speckle noise, correcting scanning artifacts and fusing images are studied as well. At the end of this paper, registration of ...

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    18. Applying a Pix2Pix Generative Adversarial Network to a Fourier-Domain Optical Coherence Tomography System for Artifact Elimination

      Applying a Pix2Pix Generative Adversarial Network to a Fourier-Domain Optical Coherence Tomography System for Artifact Elimination

      The presence of artifacts, including conjugate, DC, and auto-correlation artifacts, is a critical limitation of Fourier-domain optical coherence tomography (FD-OCT). Many methods have been proposed to resolve this problem to obtain high-quality images. Furthermore, the development of deep learning has resulted in many prospective advancements in the medical field; image-to-image translation by using generative adversarial networks (GANs) is one such advancement. In this study, we propose applying the Pix2Pix GAN to eliminate artifacts from FD-OCT images. The first experiment results showed that the proposed framework could translate conventional FD-OCT depth profiles into artifact-free FD-OCT depth profiles. In addition, the FD-OCT ...

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    19. Tunable semiconductor slotted lasers for near-infrared Optical Coherence Tomography

      Tunable semiconductor slotted lasers for near-infrared Optical Coherence Tomography

      The use of Optical Coherence Tomography in the field of clinical diagnosis is significant. There are different types of swept source lasers available on the market today, however, their design and associated complex fabrication process increase their cost. In the work presented here, an economical six-section slotted tunable laser operating near 850 nm has been designed and fabricated using a UV optical lithography process. The laser is monolithically integrable without a need for any regrowth step. Initial characterization has confirmed the high quality of the slot geometry and stable single mode operation within its tuning range.

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    20. Multi-Compartment Spatially-derived Radiomics from Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease

      Multi-Compartment Spatially-derived Radiomics from Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease

      Objective: Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) stimulates breakdown of blood-retinal barrier that causes accumulation of fluid within macula. Anti-VEGF therapy is the first-line treatment for both the diseases; however, the degree of response varies for individual patients. The main objective of this work was to identify the (i) texture-based radiomics features within individual fluid and retinal tissue compartments of baseline spectral-domain optical coherence tomography (SD-OCT) images and (ii) the specific spatial compartments that contribute most pertinent features for predicting therapeutic response ...

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    21. Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning

      Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning

      Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between two successive B-scans is computed and provided as input to the CNN. To solve the problem ...

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    22. Higher-Order Core-Like Modes in Double-Clad Fiber Contribute to Multipath Artifacts in Optical Coherence Tomography

      Higher-Order Core-Like Modes in Double-Clad Fiber Contribute to Multipath Artifacts in Optical Coherence Tomography

      Double-clad fiber (DCF) has enabled the combination of endoscopic optical coherence tomography (OCT) with secondary optical modalities. While DCF offers an additional optical channel, it is widely understood that its use reduces the quality of OCT owing to the introduction of multipath artifacts. We show here that an unexpected higher-order mode (HOM) with its energy confined to the DCF core can contribute to these artifacts. The existence of this HOM is confirmed using the spatially and spectrally (S2) resolved imaging method. The group delay difference of the HOM is shown to be consistent with the delay of the diffuse ghost ...

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    23. 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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    24. 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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    1-24 of 426 1 2 3 4 ... 16 17 18 »
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