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    1. 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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    2. 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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    3. 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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    4. 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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    5. 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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    6. 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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    7. 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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    8. 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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    9. 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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    10. 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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    11. 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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    12. 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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    13. 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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    14. 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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    15. 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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    16. B-Scan Attentive CNN for the Classification of Retinal Optical Coherence Tomography Volumes

      B-Scan Attentive CNN for the Classification of Retinal Optical Coherence Tomography Volumes

      00Optical coherence tomography (OCT) enables 3D cross-sectional imaging of the retinal tissues and has become an essential tool for the diagnosis of eye diseases. Clinically, the ophthalmologists examine each cross-sectional image (B- scan) of the 3D OCT volume to diagnose the retinal pathologies. However, this process is time-consuming and tedious. Automated methods in literature classify the individual B-scans and aggregate the diagnosis decision for the OCT volume using manual threshold-based rules. However, these methods lack generalizability and fail to incorporate the ophthalmologists’ aspects of clinical diagnosis. Therefore, in this letter, we propose a B-scan attentive convolutional neural network (BACNN) that ...

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    17. End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging

      End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging

      Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but interindividual treatment requirements are vastly heterogeneous and currently poorly plannable resulting in suboptimal treatment frequency. Optical coherence tomography (OCT) with its 3D high-resolution imaging serves as a companion diagnostic to anti-VEGF therapy. This creates a need for building predictive models using automated image analysis of OCT scans acquired during the treatment initiation phase. We propose such a model based on deep learning (DL) architecture, comprised of a densely connected neural network (DenseNet) and a recurrent neural network (RNN), trainable end-to-end. The method starts by sampling several 2D-images ...

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    18. Sparse Domain Gaussianization for Multi-variate Statistical Modeling of Retinal OCT Images

      Sparse Domain Gaussianization for Multi-variate Statistical Modeling of Retinal OCT Images

      In this paper, a multivariate statistical model that is suitable for describing Optical Coherence Tomography (OCT) images is introduced. The proposed model is comprised of a multivariate Gaussianization function in sparse domain. Such an approach has two advantages, i.e. 1) finding a function that can effectively transform the input – which is often not Gaussian – into normally distributed samples enables the reliable application of methods that assume Gaussianity, 2) although multivariate Gaussianization in spatial domain is a complicated task and rarely results in closed-form analytical model, by transferring data to sparse domain, our approach facilitates multivariate statistical modeling of OCT ...

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    19. Towards Indicating Human Skin State In Vivo Using Geometry-Dependent Spectroscopic Contrast Imaging

      Towards Indicating Human Skin State In Vivo Using Geometry-Dependent Spectroscopic Contrast Imaging

      Skin plays a significant role in human body function and its collagen states change during the human skin ageing process, which affects skin function. We previously reported on geometry-dependent spectroscopic contrast achieved by spectroscopic micro-optical coherence tomography ( S μ OCT), which discovered that transversely oriented and regularly arranged nano-cylinders selectively backscatter the long-wavelength lights and generate spectral centroid (SC) shifts towards the long wavelengths within a spectral window of 700 − 950 nm . Here we further proposed a novel method towards indicating the state of human skin in vivo using geometry-dependent spectroscopic contrast imaging. The proposed method can obtain spectroscopic contrast images ...

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    20. 3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images

      3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images

      3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to ...

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    21. Dynamic compensation of path length difference in optical coherence tomography by an automatic temperature control system of optical fiber

      Dynamic compensation of path length difference in optical coherence tomography by an automatic temperature control system of optical fiber

      Optical fiber is widely used in optical coherence tomography (OCT) to propagate light precisely with low attenuation and low dispersion. However, the total optical path length within the optical fiber varies in accordance with changes of the temperature. This leads changes in the total optical travel path of the interfering signals and results in shifting of OCT image position to an unintended depth pixel value. In this paper, we presented the temperature-based automatic path length compensating method in OCT to limit the external temperature effect and control the image position in micro-scale without manual movement of optical components. By utilizing ...

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    22. Synchronous fingerprint acquisition system based on total internal reflection and optical coherence tomography

      Synchronous fingerprint acquisition system based on total internal reflection and optical coherence tomography

      he research of external fingerprint collected by total internal reflection (TIR) has been carried out for decades and research of internal fingerprint of optical coherence tomography (OCT) has just begun. The internal fingerprint can be hardly affected by the finger surface status, due to its strong anti-interference and anti-spoofing ability, which can serve as a powerful supplement to external fingerprint. However, matching fingerprints acquired by different ways can lead to a drop in fingerprint recognition accuracy owing to the differences in fingerprint quality, distortions and detection areas. Whether the internal fingerprint can be used to replace the external fingerprint for ...

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    23. Super-Resolution of Optical Coherence Tomography Images by Scale Mixture Models

      Super-Resolution of Optical Coherence Tomography Images by Scale Mixture Models

      In this paper, a new statistical model is proposed for the single image super-resolution of retinal Optical Coherence Tomography (OCT) images. OCT imaging relies on interfero-metry, which explains why OCT images suffer from a high level of noise. Moreover, data subsampling is carried out during the acquisition of OCT A-scans and B-scans. So, it is necessary to utilize effective super-resolution algorithms to reconstruct high-resolution clean OCT images. In this paper, a nonlocal sparse model-based Bayesian framework is proposed for OCT restoration. For this reason, by characterizing nonlocal patches with similar structures, known as a group, the sparse coefficients of each ...

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    24. RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology

      RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology

      The identification of retinal lesions plays a vital role in accurately classifying and grading retinopathy. Many researchers have presented studies on optical coherence tomography (OCT) based retinal image analysis over the past. However, to the best of our knowledge, there is no framework yet available that can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive severity grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework that extracts multiple retinal lesions from OCT scans and utilizes them for lesion-influenced grading ...

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