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    1. 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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    2. 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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    3. 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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    4. 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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    5. Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal OCT Images

      Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal OCT Images

      Speckle noise is the main cause of poor optical coherence tomography (OCT) image quality. Convolutional neural networks (CNNs) have shown remarkable performances for speckle noise reduction. However, speckle noise denoising still meets great challenges because the deep learning-based methods need a large amount of labeled data whose acquisition is time-consuming or expensive. Besides, many CNNs-based methods design complex structure based networks with lots of parameters to improve the denoising performance, which consume hardware resources severely and are prone to overfitting. To solve these problems, we propose a novel semi-supervised learning based method for speckle noise denoising in retinal OCT images ...

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    6. Optical coherence tomography needle probe in neuraxial block application

      Optical coherence tomography needle probe in neuraxial block application

      Here, we overview an optical coherence tomography image-guided needle puncture based on recent results, which can help operators more accurately indicate the depth position of the puncture needle into the body, reduce subjective judgment errors, and assist the physician in operating a smoother procedure, and increase the success rate of the operation. Artificial intelligence aided image interpretation can further provide an automatic diagnosis in the future.

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    7. 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 fully 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 ...

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      Mentions: UCLA
    8. Confidence-guided Topology-preserving Layer Segmentation for Optical Coherence Tomography Images with Focus-column Module

      Confidence-guided Topology-preserving Layer Segmentation for Optical Coherence Tomography Images with Focus-column Module

      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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    9. Dual-Stage U-Shape Convolutional Network for Esophageal Tissue Segmentation in OCT Images

      Dual-Stage U-Shape Convolutional Network for Esophageal Tissue Segmentation in OCT Images

      Automatic segmentation is the crucial step for esophageal optical coherence tomography (OCT) image processing, which is able to highlight diagnosis-related tissue layers and provide characteristics such as shape and thickness for esophageal disease diagnosis. This study proposes a dual-stage framework using a specifically designed encoder-decoder network configuration for accurate and reliable esophageal layer segmentation, which is named as the dual-stage U-shape convolutional network (D-UCN). The proposed approach utilized one UCN to locate the target tissue region, which is followed by another UCN with similar architecture to achieve the final segmentation. In this way, the proposed strategy effectively solves the problems ...

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    10. ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

      ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

      Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE ...

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    11. Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images

      Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images

      Recent studies suggest that deep learning systems can now achieve performance on par with medical experts in diagnosis of disease. A prime example is in the field of ophthalmology, where convolutional neural networks (CNNs) have been used to detect retinal and ocular diseases. However, this type of artificial intelligence (AI) has yet to be adopted clinically due to questions regarding robustness of the algorithms to datasets collected at new clinical sites and a lack of explainability of AI-based predictions, especially relative to those of human expert counterparts. In this work, we develop CNN architectures that demonstrate robust detection of glaucoma ...

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    12. Broadband Coupler Manipulation through Particle Swarm Optimization for Arrayed Waveguide Grating based Optical Coherence Tomography

      Broadband Coupler Manipulation through Particle Swarm Optimization for Arrayed Waveguide Grating based Optical Coherence Tomography

      The state-of-the-art optical coherence technology (OCT) has its limitation, such as bulky and quite expensive. Miniaturization and integration of the OCT system are promising to get a compact, less costly, and more stable device. In this paper, we report the influence of optical power splitting on the sensitivity and axial resolution in spectral-domain OCT (SD-OCT) on silicon chip using tandem Mach-Zehnder directional coupler (MZDC) based broadband coupler through particle swarm optimization (PSO). A less-flat wavelength response from a broadband coupler in an interferometer for SD-OCT on-chip will decrease the sensing sensitivity. The arrayed waveguide grating (AWG) as a spectrometer in ...

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    13. Image sensor for spectral-domain optical coherence tomography on a chip

      Image sensor for spectral-domain optical coherence tomography on a chip

      This Letter presents a global shutter image sensor based on PIN photodiodes intended to be used for optical coherence tomography (OCT) in the spectral domain, where the light is brought to the photodiodes via optical waveguides monolithically integrated on top of electronics in a photonic layer. Photodiodes are optimised to have the peak responsivity in the wavelength range between 800 and 900 nm, while the main features of electronics are high signal-to-noise ratio, high frame rate, low-power consumption and low noise. The sensor is designed using 0.35 µm high voltage CMOS and employing low doped epitaxial starting material in ...

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    14. Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method

      Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method

      This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a low-rank regularization approach is proposed for exploiting nonlocal self-similarity prior to OCT image reconstruction. To benefit from ...

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    15. Axial Super-Resolution Study for Optical Coherence Tomography Images via Deep Learning

      Axial Super-Resolution Study for Optical Coherence Tomography Images via Deep Learning

      Optical coherence tomography (OCT) is a noninvasive, high resolution, and real-time imaging technology that has been used in ophthalmology and other medical fields. Limited by the point spread function of OCT system, it is difficult to optimize its spatial resolution only based on hardware. Digital image processing methods, especially deep learning, provide great potential in super-resolving images. In this paper, the matched axial low resolution (LR) and high resolution OCT image pairs from actual OCT imaging are collected to generate the dataset by our home-made spectral domain OCT (SD-OCT) system. Several methods are selected to super-resolve LR OCT images. It ...

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    16. Ultra-high-resolution 3D optical coherence tomography reveals inner structures of human placenta-derived trophoblast organoids

      Ultra-high-resolution 3D optical coherence tomography reveals inner structures of human placenta-derived trophoblast organoids

      Objective: 3D optical coherence tomography (OCT) is used for analyses of human placenta organoids in situ without sample preparation. Methods: The trophoblast organoids analyzed were derived from primary human trophoblast. In this study a custom made ultra-high-resolution spectral domain OCT system with uniform spatial and axial resolution of 2.48 m in organoid tissue was used. The obtained OCT results align to differentiation status tested via quantitative polymerase chain reaction, Western blot analyses, immunohistochemistry, and immunofluorescence of histological sections. Results: 3D OCT enables a more detailed placenta organoid monitoring compared to brightfield microscopy. Inner architecture with light scattering bridges surrounding ...

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    17. 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 paper, 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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    18. A Generic Framework for Fourier-Domain Optical Coherence Tomography Imaging: Software Architecture and Hardware Implementations

      A Generic Framework for Fourier-Domain Optical Coherence Tomography Imaging: Software Architecture and Hardware Implementations

      Fourier-domain optical coherence tomography (FD-OCT), including spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT), allows the volumetric imaging of the tissue architecture with a faster speed and higher detection sensitivity than does time-domain OCT. Although the hardware implementations of SD-OCT and SS-OCT are different, these technologies share very similar signal processing steps for image reconstruction. In this study, we developed hardware implementations and software architectures to design a generic framework for FD-OCT. For SD-OCT systems, an external synchronization approach was used to realize a data acquisition schematic similar to that used in SS-OCT by carefully managing the timing clocks in the ...

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    19. Vision-Inspection-Synchronized Dual Optical Coherence Tomography for High-Resolution Real-Time Multidimensional Defect Tracking in Optical Thin Film Industry

      Vision-Inspection-Synchronized Dual Optical Coherence Tomography for High-Resolution Real-Time Multidimensional Defect Tracking in Optical Thin Film Industry

      Large-scale product inspection is an important aspect in thin film industry to identify defects with a high precision. Although vision line scan camera (VLSC)-based inspection has been frequently implemented, it is limited to surface inspections. Therefore, to overcome the conventional drawbacks, there is a need to extend inspection capabilities to internal structures. Considering that VLSC systems have access to rich information, such as color and texture, high-resolution real-time multimodal optical synchronization between VLSC and dual spectral domain optical coherence tomography (SD-OCT) systems was developed with a laboratory customized in-built automated defect-tracking algorithm for optical thin films (OTFs). Distinguishable differences ...

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    20. Image Projection Network: 3D to 2D Image Segmentation in OCTA Images

      Image Projection Network: 3D to 2D Image Segmentation in OCTA Images

      We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network ...

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    21. 2D Ultrasonic Array-based Optical Coherence Elastography

      2D Ultrasonic Array-based Optical Coherence Elastography

      Acoustic radiation force optical coherence elastography (ARF-OCE) has been successfully implemented to characterize the biomechanical properties of soft tissues such as the cornea and the retina with high resolution using single-element ultrasonic transducers for ARF excitation. Most currently proposed OCE techniques, such as air-puff and ARF, have less capability to control the spatiotemporal information of the induced region of deformation, resulting in limited accuracy and low temporal resolution of the shear wave elasticity imaging. In this study, we propose a new method called 2D ultrasonic array-based optical coherence elastography imaging, which combines the advantages of 3D dynamic electronic steering of ...

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    22. Intra-operative Optical Coherence Imaging of In-vivo Chronic Otitis Media followed by Post-operative Audiogram Assessments

      Intra-operative Optical Coherence Imaging of In-vivo Chronic Otitis Media followed by Post-operative Audiogram Assessments

      The successful surgery of chronic otitis media (COM) is challenging; this depends on the surgeon's knowledge of the optical visibility of surgical microscopes. Herein, we reported the utilization of intra-surgical optical coherence tomography (OCT) system to effectively guide the surgery of COM based on augmented reality with cross-sectional images. The intra-surgical spectral-domain OCT system with a center wavelength of 846 nm was capable of obtaining non-invasive, high-resolution, and high-speed visualizations with an axial resolution of 8 μm, lateral resolution of 30 μm, and an extended working distance of 280 mm. Three patients with COM were involved in this research ...

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    23. SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images

      SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images

      Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer to groundtruth images in the feature space, while the discriminator ...

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    24. Unsupervised Denoising of Optical Coherence Tomography Images with Nonlocal-Generative Adversarial Network

      Unsupervised Denoising of Optical Coherence Tomography Images with Nonlocal-Generative Adversarial Network

      Deep learning for image denoising has recently attracted considerable attentions due to its excellent performance. Since most of current deep learning based denoising models require a large number of clean images for training, it is difficult to extend them to the denoising problems when the reference clean images are hard to acquire (e.g., optical coherence tomography (OCT) images). In this paper, we propose a novel unsupervised deep learning model called as nonlocal-generative adversarial network (Nonlocal-GAN) for OCT image denoising, where the deep model can be trained without reference clean images. Specifically, considering that the background areas of OCT images ...

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