1. 1-24 of 448 1 2 3 4 ... 17 18 19 »
    1. Ultra-widefield OCT Angiography

      Ultra-widefield OCT Angiography

      Optical Coherence Tomography Angiography (OCTA), a functional extension of OCT, has the potential to replace most invasive fluorescein angiography (FA) exams in ophthalmology. So far, OCTA’s field of view is however still lacking behind fluorescence fundus photography techniques. This is problematic, because many retinal diseases manifest at an early stage by changes of the peripheral retinal capillary network. It is therefore desirable to expand OCTA’s field of view to match that of ultra-widefield fundus cameras. We present a custom developed clinical high-speed swept-source OCT (SS-OCT) system operating at an acquisition rate 8-16 times faster than today’s state-of-the-art ...

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    2. Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification

      Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification

      Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients. An Attention U-Net is trained to output a probability ...

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    3. A Star Shape Prior Based Regularizer for Vessel Lumen Segmentation in OCT Images

      A Star Shape Prior Based Regularizer for Vessel Lumen Segmentation in OCT Images

      Optical coherence tomography (OCT) is widely used in high-resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessel lumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSP-Regularizer is ...

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    4. Joint Motion Correction and 3D Segmentation with Graph-Assisted Neural Networks for Retinal OCT

      Joint Motion Correction and 3D Segmentation with Graph-Assisted Neural Networks for Retinal OCT

      Optical Coherence Tomography (OCT) is a widely used non- invasive high resolution 3D imaging technique for biological tis- sues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT- Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion ...

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      Mentions: UCSD
    5. AV-casNet: Fully Automatic Arteriole-Venule Segmentation and Differentiation in OCT Angiography

      AV-casNet: Fully Automatic Arteriole-Venule Segmentation and Differentiation in OCT Angiography

      Automatic segmentation and differentiation of retinal arteriole and venule (AV), defined as small blood vessels directly before and after the capillary plexus, are of great importance for the diagnosis of various eye diseases and systemic diseases, such as diabetic retinopathy, hypertension, and cardiovascular diseases. Optical coherence tomography angiography (OCTA) is a recent imaging modality that provides capillary-level blood flow information. However, OCTA does not have the colorimetric and geometric differences between AV as the fundus photography does. Various methods have been proposed to differentiate AV in OCTA, which typically needs the guidance of other imaging modalities. In this study, we ...

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    6. 400 Hz Volume Rate Swept-Source Optical Coherence Tomography at 1060 nm Using a KTN Deflector

      400 Hz Volume Rate Swept-Source Optical Coherence Tomography at 1060 nm Using a KTN Deflector

      In this Letter, a swept-source optical coherence tomography (SS-OCT) instrument employing an innovative scanning protocol for high-speed volumetric rate imaging is demonstrated. The optical source is a tunable laser based on a supercontinuum source pumped with femtosecond pulses, followed by a time-stretched delay fiber. The instrument is equipped with an ultra-fast lateral scanner, based on a KTN crystal, driven at 100 kHz. The letter proves the utility of combining an ultra-fast lateral scanner with an ultra-fast swept laser to provide A-scans at a repetition rate of 40 MHz and an unprecedented 3D-OCT volume acquisition rate of 400 Hz.

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    7. Superior Imaging Performance of All-Fiber, Two-Focusing-Element Microendoscopes

      Superior Imaging Performance of All-Fiber, Two-Focusing-Element Microendoscopes

      All-fiber-optic imaging microendoscopes are emerging as an important tool in bioimaging studies, including those conducted with optical coherence tomography, but physical limitations constrain the achievable beam characteristics of designs using a single focusing element. These constraints are especially relevant for applications that require a long working distance, high resolution, and/or minimal probe diameter. Through detailed analysis based on ABCD matrix modelling, we show that side-viewing probes combining a graded-index (GRIN) fiber with a ball lens – GRIN-ball-lens probes (GBLPs) – offer superior performance over a range of numerical apertures and pave the way for a broader range of imaging applications. The ...

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    8. Progressive Feature Fusion Attention Dense Network for Speckle Noise Removal in OCT Images

      Progressive Feature Fusion Attention Dense Network for Speckle Noise Removal in OCT Images

      Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep ...

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

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

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

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    10. Evaluation of signal degradation due to birefringence in a multiple reference optical coherence tomography system with polarization-based balanced detection

      Evaluation of signal degradation due to birefringence in a multiple reference optical coherence tomography system with polarization-based balanced detection

      Although time-domain optical coherence tomog- raphy (TD-OCT) systems are straightforward to realize, the imaging speed, sensitivity, and imaging depth limit their range of applications. Multiple reference optical coherence tomography (MR-OCT) based on TD-OCT increases imaging range by about tenfold while providing sensitivity to image highly scattering biological samples. The multiple path-delays and free- space construction make MR-OCT also interesting for hybrid and compact systems, filling the gap between fibre-based and wafer-level integrated optical systems. We describe an optical configuration using a balanced detection scheme and the resulting signal properties due to the required use of polarizing optical components. We numerically ...

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    11. Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

      Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

      Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for the reconstruction of realistic 6 × 6 m m 2 /low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simple degradation of the 3 × 3 m m 2 /high-resolution (HR) image to ...

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    12. Disentangled Representation Learning for OCTA Vessel Segmentation with Limited Training Data

      Disentangled Representation Learning for OCTA Vessel Segmentation with Limited Training Data

      Optical coherence tomography angiography (OCTA) is an imaging modality that can be used for analyzing retinal vasculature. Quantitative assessment of en face OCTA images requires accurate segmentation of the capillaries. Using deep learning approaches for this task faces two major challenges. First, acquiring sufficient manual delineations for training can take hundreds of hours. Second, OCTA images suffer from numerous contrast-related artifacts that are currently inherent to the modality and vary dramatically across scanners. We propose to solve both problems by learning a disentanglement of an anatomy component and a local contrast component from paired OCTA scans. With the contrast removed ...

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    13. Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT

      Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT

      Objective/background: In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo. Methods: We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 µm (in tissue) with an improved contrast and a custom deep residual ...

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    14. 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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    15. 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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    16. 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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    17. 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
    18. 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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    19. 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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    20. 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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    21. 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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    22. 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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    23. 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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    24. 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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    1-24 of 448 1 2 3 4 ... 17 18 19 »
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