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    1. 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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    2. 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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    3. 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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    4. 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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    5. 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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    6. 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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    7. 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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    8. 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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    9. 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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    10. 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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    11. 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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    12. Denoising Performance Evaluation of Automated Age-related Macular Degeneration Detection on Optical Coherence Tomography Images

      Denoising Performance Evaluation of Automated Age-related Macular Degeneration Detection on Optical Coherence Tomography Images

      Automated detection of eye diseases using artificial intelligence techniques on optical coherence tomography (OCT) images is widely researched in the field of ophthalmology. Such detections are usually performed with the aid of computers. Using high-level simulations, this study investigates and evaluates three automated age-related macular degeneration (AMD) detection flows in terms of computation time and detection accuracy for future hardware-accelerated designs of intelligent and portable OCT systems. In this study, a block-matching and 3-Dimension filter (BM3DF), a hybrid median filter (HMF), and an adaptive wiener filter (AWF) are used to denoise the OCT images. Support vector machine (SVM), AlexNet, GoogLeNet ...

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    13. Analysis of A Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations

      Analysis of A Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations

      One of the newest important medical imaging modalities is Optical Coherence Tomography (OCT) providing the possibility of taking pictures from optical scattering tissues such as retina. With the help of accurate verification and analysis of OCT images, it is possible to identify and treat irreversible retinal diseases like glaucoma. Therefore, to suggest novel high-performance segmentation methods is of significant importance. In this paper, a novel algorithm is proposed for the segmentation of OCT B-scans. The proposed method uses the intensity information of pixels to find a distinguishing feature for boundary pixels which are located on the retinal layer boundaries. Also ...

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    14. Surface and Internal Fingerprint Reconstruction from Optical Coherence Tomography through Convolutional Neural Network

      Surface and Internal Fingerprint Reconstruction from Optical Coherence Tomography through Convolutional Neural Network

      Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this paper, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To ...

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    15. Full-Range Line-Field Optical Coherence Tomography for High-Accuracy Measurements of Optical Lens

      Full-Range Line-Field Optical Coherence Tomography for High-Accuracy Measurements of Optical Lens

      A full-range line-field Fourier-domain optical coherence tomography (LF-FDOCT) system with an accuracy at nanoscale was proposed for high-accuracy measurements of optical lens surface profile. The LF surface curve information of optical lens could be obtained in one measurement which does not need point-by-point scanning used in the traditional 1-D FDOCT system. The measurement accuracy of surface profile curve could be improved from micrometer scale to nanoscale by applying spectral center correction method (SCCM) to the complex spectral interferogram. The corrected five phase-shifting (CFPS) method was employed to eliminate the complex-conjugate artifacts and the polychromatic error. The numerical simulation and experiment ...

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    16. Fabrication of Dental Crown by Optical Coherence Tomography: A Pilot Study

      Fabrication of Dental Crown by Optical Coherence Tomography: A Pilot Study

      Digital impressions have been studied for better gingival retraction in including the under subgingival finish line condition. Here, we employed swept-source optical coherence tomography (SS-OCT) of 1310 nm wavelength, which is capable of noninvasive, high-resolution, and high-speed, to discern the utilization-possibility for supporting the fabrication of the dental crown. A three-dimensional (3D) abutment was used at the 0.5 mm of the subgingival finish line below the level of the gingiva. The SS-OCT system scanned a field of view of 10 mm × 10 mm using the 3D working model by the depth-directional three focal points. The obtained 1500 images of ...

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    17. Cooperative low-rank models for removing stripe noise from OCTA images

      Cooperative low-rank models for removing stripe noise from OCTA images

      Optical coherence tomography angiography (OCTA) is an emerging non-invasive imaging technique for imaging the microvasculature of the eye based on phase variance or amplitude decorrelation derived from repeated OCT images of the same tissue area. Stripe noise occurs during the OCTA acquisition process due to the involuntary movement of the eye. To remove the stripe noise (or 'destriping') effectively, we propose two novel image decomposition models to simultaneously destripe all the OCTA images of the same eye in a cooperative way: cooperative uniformity destriping (CUD) and cooperative similarity destriping (CSD) model. Both the models consider stripe noise by low-rank constraint ...

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    18. MS-CAM: Multi-Scale Class Activation Maps for Weakly-supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images

      MS-CAM: Multi-Scale Class Activation Maps for Weakly-supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images

      As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions affected by GA is a fundamental step for clinical diagnosis. In this paper, we present a novel weakly supervised model for GA segmentation in Spectral-Domain Optical Coherence Tomography (SD-OCT) images. A novel Multi-Scale Class Activation Map (MS-CAM) is proposed to highlight the discriminatory significance regions in localization and detail descriptions. To extract available multi-scale features, we design a Scaling and UpSampling (SUS) module to balance ...

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    19. Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images

      Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images

      The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore ...

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    20. Machine Learning Techniques for Ophthalmic Data Processing: A Review

      Machine Learning Techniques for Ophthalmic Data Processing: A Review

      Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma. The review covers over 60 publications and 25 public datasets and challenges related to the detection, grading, and lesion segmentation of the three considered diseases. Each section provides a summary of the public datasets and challenges related to each pathology and the current methods that have been applied to the problem. Furthermore, the recent machine ...

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    21. Non-invasive optical screening of Streptococcus Pneumonia based inflammatory changes of the tympanic membrane and mastoid mucosa in guinea pig otitis media using optical coherence tomography

      Non-invasive optical screening of Streptococcus Pneumonia based inflammatory changes of the tympanic membrane and mastoid mucosa in guinea pig otitis media using optical coherence tomography

      Abstract: The accurate screening of otitis media (OM) lies in clarifying the numerous confounding and quantitative factors that are discovered during primary inspections. Increased awareness about bacterial biofilms and inflammation has allowed researchers to develop a better understanding of the bacterial infections that occur in the middle ear. In this study, four live guinea pigs were inoculated with Streptococcus pneumonia to induce OM-related inflammatory changes. Since optical techniques have been effectively used for diagnosis in medicine, low-coherence interferometry-based optical coherence tomography (OCT) was employed for depth-resolved high-resolution data screening. Multiple locations of the tympanic membrane (TM), mastoid mucosa, and round ...

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    22. Multivariate Statistical Modeling of Retinal Optical Coherence Tomography

      Multivariate Statistical Modeling of Retinal Optical Coherence Tomography

      In this paper a new statistical multivariate model for retinal Optical Coherence Tomography (OCT) Bscans is proposed. Due to the layered structure of OCT images, there is a horizontal dependency between adjacent pixels at specific distances, which led us to propose a more accurate multivariate statistical model to be employed in OCT processing applications such as denoising. Due to the asymmetric form of the probability density function (pdf) in each retinal layer, a generalized version of multivariate Gaussian Scale Mixture (GSM) model, which we refer to as GM-GSM model, is proposed for each retinal layer. In this model, the pixel ...

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    23. Assessment of the Inner Surface Roughness of 3D Printed Dental Crowns via Optical Coherence Tomography Using a Roughness Quantification Algorithm

      Assessment of the Inner Surface Roughness of 3D Printed Dental Crowns via Optical Coherence Tomography Using a Roughness Quantification Algorithm

      Abstract: Dental crowns are used to restore decayed or chipped teeth, where their surfaces play a key role in this restoration process, as they affect the fitting and stable bonding of the prostheses. The surface texture of crowns can interfere with this restoration process, therefore the measurement of their inner surface roughness is very important but difficult to achieve using conventional imaging methods. In this study, the inner surfaces of dental crowns were three-dimensionally (3D) visualized using swept-source optical coherence tomography (SS-OCT) system. Nine crowns were fabricated with a commercial 3D printer using three different hatching methods (one-way, cross, and ...

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    24. Automated In Vivo Navigation of Magnetic-Driven Microrobots Using OCT Imaging Feedback

      Automated In Vivo Navigation of Magnetic-Driven Microrobots Using OCT Imaging Feedback

      Objective: The application of in vivo microrobot navigation has received considerable attention from the field of precision therapy, which uses microrobots in living organisms. Methods: This study investigates the navigation of microrobots in vivo using optical coherence tomography (OCT) imaging feedback. The electromagnetic gradient field generated by a home-made electromagnetic manipulation system is magnetically modeled. With this model, the magnetic force acting on the microrobot is calculated, and the relationship between this force and the velocity of the microrobot is characterized. Results: Results are verified through in vitro experiments wherein microrobots are driven in three types of fluid, namely, normal ...

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