1. 1-24 of 435 1 2 3 4 ... 17 18 19 »
    1. Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography

      Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography

      Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the ...

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    2. Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning

      Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning

      Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed ...

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    3. Volumetric characterization of microvasculature in ex vivo human brain samples by serial sectioning optical coherence tomography

      Volumetric characterization of microvasculature in ex vivo human brain samples by serial sectioning optical coherence tomography

      Objective: Serial sectioning optical coherence tomography (OCT) enables distortion-free volumetric reconstruction of several cubic centimeters of human brain samples. We aimed to identify anatomical features of the ex vivo human brain, such as intraparenchymal blood vessels and axonal fiber bundles, from the OCT data in 3D, using intrinsic optical contrast. Methods: We developed an automatic processing pipeline to enable characterization of the intraparenchymal microvascular network in human brain samples. Results: We demonstrated the automatic extraction of the vessels down to a 20 m in diameter using a filtering strategy followed by a graphing representation and characterization of the geometrical properties ...

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    4. Automated Retinal Vein Cannulation on Silicone Phantoms Using Optical-Coherence-Tomography-Guided Robotic Manipulations

      Automated Retinal Vein Cannulation on Silicone Phantoms Using Optical-Coherence-Tomography-Guided Robotic Manipulations

      Retinal vein occlusion is one of the most common causes of vision loss, occurring when a blood clot or other obstruction occludes a retinal vein. A potential remedy for retinal vein occlusion is retinal vein cannulation, a surgical procedure that involves infusing the occluded vein with a fibrinolytic drug to restore blood flow through the vascular lumen. This work presents an image-guided robotic system capable of performing automated cannulation on silicone retinal vein phantoms. The system is integrated with an optical coherence tomography probe and camera to provide visual feedback to guide the robotic system. Through automation, the developed system ...

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      Mentions: UCLA
    5. Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy

      Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy

      Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier's accuracy is dependent on the quality of the OCT image. Therefore, image denoising plays an important role in having an accurate feedback system. A common OCT image denoising technique is the frame-averaging method. Inherent to this method is the ...

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    6. Self-Supervised Sequence Recovery for Semi-Supervised Retinal Layer Segmentation

      Self-Supervised Sequence Recovery for Semi-Supervised Retinal Layer Segmentation

      Automated layer segmentation plays an important role for retinal disease diagnosis in optical coherence tomography (OCT) images. However, the severe retinal diseases result in the performance degeneration of automated layer segmentation approaches. In this paper, we present a robust semi-supervised retinal layer segmentation network to relieve the model failures on abnormal retinas, in which we obtain the lesion features from the labeled images with disease-balanced distribution, and utilize the unlabeled images to supplement the layer structure information. Specifically, in our proposed method, the cross-consistency training is utilized over the predictions of the different decoders, and we enforce a consistency between ...

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    7. Robust Fovea Detection in Retinal OCT Imaging using Deep Learning

      Robust Fovea Detection in Retinal OCT Imaging using Deep Learning

      The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes is important for assessing visual function correlates and treatment guidance in macular disease. In this study, the "PRE U-net" is introduced as a novel approach for a fully automated fovea centralis detection, addressing the localization as a pixel-wise regression task. 2D B-scans are sampled from each image volume and are concatenated with spatial location information to train the deep network. A total of 5586 ...

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    8. Integrated US-OCT-NIRF Tri-modality Endoscopic Imaging System for Pancreaticobiliary Duct Imaging

      Integrated US-OCT-NIRF Tri-modality Endoscopic Imaging System for Pancreaticobiliary Duct Imaging

      Pancreaticobiliary carcinomas is a highly malignant gastrointestinal tumor. Most pancreaticobiliary cancers arise from epithelial proliferations within the pancreaticobiliary ducts, referred to as pancreatic intraepithelial neoplasias (PanINs). Some PanINs are benign metaplasia, while others progress to invasive duct adenocarcinoma (IDAC). However, there is no standard programme to diagnose the progression from PanINs to IDAC. In this study, we present a tri-modality imaging system, which integrates ultrasound (US), optical coherence tomography (OCT), and near-infrared fluorescence (NIRF) for pancreaticobiliary duct imaging. This system can obtain OCT, US, and NIRF images in real time with a frame rate of 30 frames per second. For ...

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    9. High-speed balanced-detection visible-light optical coherence tomography in the human retina using subpixel spectrometer calibration

      High-speed balanced-detection visible-light optical coherence tomography in the human retina using subpixel spectrometer calibration

      Increases in speed and sensitivity enabled rapid clinical adoption of optical coherence tomography (OCT) in ophthalmology. Recently, visible-light OCT (vis-OCT) achieved ultrahigh axial resolution, improved tissue contrast, and provided new functional imaging capabilities, demonstrating the potential to improve clinical care further. However, limited speed and sensitivity caused by the high relative intensity noise (RIN) in supercontinuum lasers impeded the clinical adoption of vis-OCT. To overcome these limitations, we developed balanced-detection vis-OCT (BD-vis-OCT), which uses two calibrated spectrometers to cancel RIN and other noises. We analyzed the RIN to achieve robust subpixel calibration between the two spectrometers and showed that BD-vis-OCT ...

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    10. 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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    11. 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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    12. 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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    13. 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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    14. 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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    15. 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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    16. 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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    17. 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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    18. 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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    19. 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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    20. 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
    21. 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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    22. 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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    23. 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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    24. 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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    1-24 of 435 1 2 3 4 ... 17 18 19 »
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