1. Articles from Sina Farsiu

    1-24 of 96 1 2 3 4 »

    1. Validation of a deep learning-based algorithm for segmentation of the ellipsoid zone on optical coherence tomography images of an USH2A-related retinal degeneration clinical trial

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      Validation of a deep learning-based algorithm for segmentation of the ellipsoid zone on optical coherence tomography images of an USH2A-related retinal degeneration clinical trial

      Purpose: To assess the generalizability of a deep learning-based algorithm to segment the ellipsoid zone (EZ). Methods: The dataset consisted of 127 spectral-domain optical coherence tomography volumes from eyes of participants with USH2A-related retinal degeneration enrolled in the RUSH2A clinical trial ( NCT03146078 ). The EZ was segmented manually by trained Readers and automatically by DOCTAD, a deep learning-based algorithm originally developed for macular telangiectasia type 2. Performance was evaluated using the Dice similarity coefficient (DSC) between the segmentations, and the absolute difference and Pearson's correlation of measurements of interest obtained from the segmentations. Results: With DOCTAD, the average (mean ± SD ...

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      Mentions: Duke University UCSF

    2. Computational 3D microscopy with optical coherence refraction tomography

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      Computational 3D microscopy with optical coherence refraction tomography

      Optical coherence tomography (OCT) has seen widespread success as an in vivo clinical diagnostic 3D imaging modality, impacting areas including ophthalmology, cardiology, and gastroenterology. Despite its many advantages, such as high sensitivity, speed, and depth penetration, OCT suffers from several shortcomings that ultimately limit its utility as a 3D microscopy tool, such as its pervasive coherent speckle noise and poor lateral resolution required to maintain millimeter-scale imaging depths. Here, we present 3D optical coherence refraction tomography (OCRT), a computational extension of OCT that synthesizes an incoherent contrast mechanism by combining multiple OCT volumes, acquired across two rotation axes, to form ...

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    3. Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images

      Explore ScienceDirect Ophthalmology
      Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images

      The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentation of the Schlemm's canal (SC) lumen in optical coherence tomography (OCT) scans of living mouse eyes. A novel convolutional neural network (CNN) for semantic segmentation grounded in a U-Net architecture was developed by incorporating a late fusion scheme, multi-scale input image pyramid, dilated residual convolution blocks, and attention-gating. 163 pairs of intensity and speckle variance (SV) OCT B-scans acquired from 32 living mouse eyes were used for training, validation, and testing of this CNN model for segmentation of ...

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      Mentions: Duke University

    4. Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images

      Explore OSA | OSA Publishing Gastroenterology
      Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images

      Optical coherence tomography (OCT) is used for diagnosis of esophageal diseases such as Barrett's esophagus. Given the large volume of OCT data acquired, automated analysis is needed. Here we propose a bilateral connectivity-based neural network for in vivo human esophageal OCT layer segmentation. Our method, connectivity-based CE-Net (Bicon-CE), defines layer segmentation as a combination of pixel connectivity modeling and pixel-wise tissue classification. Bicon-CE outperformed other widely used neural networks and reduced common topological prediction issues in tissues from healthy patients and from patients with Barrett's esophagus. This is the first end-to-end learning method developed for automatic segmentation of ...

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      Mentions: Duke University University of North Carolina

    6. Unified k-space theory of optical coherence tomography

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      Unified k-space theory of optical coherence tomography

      We present a general theory of optical coherence tomography (OCT), which synthesizes the fundamental concepts and implementations of OCT under a common 3D k -space framework. At the heart of this analysis is the Fourier diffraction theorem, which relates the coherent interaction between a sample and plane wave to the Ewald sphere in the 3D k space representation of the sample. While only the axial dimension of OCT is typically analyzed in k -space, we show that embracing a fully 3D k space formalism allows explanation of nearly every fundamental physical phenomenon or property of OCT, including contrast mechanism, resolution ...

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      Mentions: Duke University

    7. Microscope-Integrated OCT-Guided Volumetric Measurements of Subretinal Blebs Created by a Suprachoroidal Approach

      Explore TVST Journal Ophthalmology
      Microscope-Integrated OCT-Guided Volumetric Measurements of Subretinal Blebs Created by a Suprachoroidal Approach

      Purpose: To investigate the use of imaging modalities in the volumetric measurement of the subretinal space and examine the volume of subretinal blebs created by a subretinal drug delivery device utilizing microscope-integrated optical coherence tomography (MIOCT). Methods: An MIOCT image-based volume measurement method was developed and assessed for accuracy and reproducibility by imaging ceramic spheres of known size that were surgically implanted into ex vivo porcine eyes. This method was then used to measure subretinal blebs created in 10 porcine eyes by injection of balanced salt solution utilizing a subretinal delivery device via a suprachoroidal cannula. Bleb volumes obtained from ...

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      Mentions: Duke University

    8. Microscope-Integrated OCT-Guided Volumetric Measurements of Subretinal Blebs Created by a Suprachoroidal Approach

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      Microscope-Integrated OCT-Guided Volumetric Measurements of Subretinal Blebs Created by a Suprachoroidal Approach

      Purpose: To investigate the use of imaging modalities in the volumetric measurement of the subretinal space and examine the volume of subretinal blebs created by a subretinal drug delivery device utilizing microscope-integrated optical coherence tomography (MIOCT). Methods: An MIOCT image-based volume measurement method was developed and assessed for accuracy and reproducibility by imaging ceramic spheres of known size that were surgically implanted into ex vivo porcine eyes. This method was then used to measure subretinal blebs created in 10 porcine eyes by injection of balanced salt solution utilizing a subretinal delivery device via a suprachoroidal cannula. Bleb volumes obtained from ...

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    9. Weakly supervised individual ganglion cell segmentation from adaptive optics OCT images for glaucomatous damage assessment

      Explore OSA | OSA Publishing Ophthalmology
      Weakly supervised individual ganglion cell segmentation from adaptive optics OCT images for glaucomatous damage assessment

      Cell-level quantitative features of retinal ganglion cells (GCs) are potentially important biomarkers for improved diagnosis and treatment monitoring of neurodegenerative diseases such as glaucoma, Parkinson’s disease, and Alzheimer’s disease. Yet, due to limited resolution, individual GCs cannot be visualized by commonly used ophthalmic imaging systems, including optical coherence tomography (OCT), and assessment is limited to gross layer thickness analysis. Adaptive optics OCT (AO-OCT) enables in vivo imaging of individual retinal GCs. We present an automated segmentation of GC layer (GCL) somas from AO-OCT volumes based on weakly supervised deep learning (named WeakGCSeg), which effectively utilizes weak annotations in ...

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      Mentions: Duke University FDA Indiana University

    11. COMPARISON OF SINGLE DRUSEN SIZE ON COLOR FUNDUS PHOTOGRAPHY AND SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY

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      COMPARISON OF SINGLE DRUSEN SIZE ON COLOR FUNDUS PHOTOGRAPHY AND SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY

      Purpose: To determine the relationship of drusen size as determined by spectral domain optical coherence tomography ( SD-OCT ), with that measured on registered Color fundus photography (CFP) images, to derive an OCT-based classification system that was comparable to that determined by CFP. Methods: Custom software was developed to register CFP images to the scanning laser ophthalmoscopy fundus images obtained simultaneously with the corresponding SD-OCT images, so that individual drusen observed on CFP could be matched with those seen on SD-OCT . Single druse size (diameter, area, volume, height) on CFP and SD-OCT images from a phase 2 clinical trial was determined with ...

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      Mentions: Duke University

    12. Local anatomic precursors to new onset geographic atrophy in age-related macular degeneration as defined on optical coherence tomography

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      Local anatomic precursors to new onset geographic atrophy in age-related macular degeneration as defined on optical coherence tomography

      Purpose In macula-wide analyses, spectral domain optical coherence tomography (SDOCT) features such as drusen volume, hyperreflective foci and OCT-reflective drusen substructures independently predict onset of geographic atrophy (GA) secondary to age-related macular degeneration (AMD). We sought to identify SDOCT features in the location of new GA prior to its onset. Design Retrospective study Subjects Age-Related Eye Disease Study 2 Ancillary SDOCT Study Participants Methods We analyzed longitudinally-captured SDOCT and color photographs from 488 eyes (of 488 participants) with intermediate AMD at baseline. Sixty-two eyes with sufficient image quality demonstrated new onset GA on color photographs during study years two through ...

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      Mentions: Duke University

    13. Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2

      Explore BJO Online Ophthalmology
      Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2

      Aim: To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2). Methods: The dataset consisted of 99 eyes from 67 participants enrolled in an international, multicentre, phase 2 MacTel2 clinical trial ( NCT01949324 ). Each eye was imaged with spectral-domain OCT at three time points over 2 years. Retinal cavitations were manually segmented by a trained Reader and the retinal cavitation volume was calculated. Two convolutional neural networks (CNNs) were developed that operated in sequential stages. In the first stage, CNN1 classified whether a B-scan contained any retinal cavitations. In ...

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      Mentions: National Institutes of Health Duke University

    14. Lightweight Learning-based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images

      Explore American Journal of Ophthalmology Ophthalmology
      Lightweight Learning-based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images

      Purpose Subretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate dosing of therapeutics at target locations is crucial but difficult to achieve using subretinal injections due to leakage, and there is no method available to measure the volume of therapeutics successfully administered to the subretinal location during surgery. Here we introduce the first automatic method for quantifying the volume of subretinal blebs, using porcine eyes injected with Ringer’s lactate solution as samples. Design Experimental study. Methods Microscope-integrated optical coherence tomography was utilized to obtain 3D visualization of subretinal blebs in porcine eyes at Duke Eye Center ...

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      Mentions: Duke University Tianjin University

    15. Spectroscopic optical coherence refraction tomography

      Explore OSA | OSA Publishing
      Spectroscopic optical coherence refraction tomography

      In optical coherence tomography (OCT), the axial resolution is often superior to the lateral resolution, which is sacrificed for long imaging depths. To address this anisotropy, we previously developed optical coherence refraction tomography (OCRT), which uses images from multiple angles to computationally reconstruct an image with isotropic resolution, given by the OCT axial resolution. On the other hand, spectroscopic OCT (SOCT), an extension of OCT, trades axial resolution for spectral resolution and hence often has superior lateral resolution. Here, we present spectroscopic OCRT (SOCRT), which uses SOCT images from multiple angles to reconstruct a spectroscopic image with isotropic spatial resolution ...

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      Mentions: Duke University

    16. Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema

      Explore OSA | OSA Publishing Ophthalmology
      Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema

      Anti-vascular endothelial growth factor (VEGF) agents are widely regarded as the first line of therapy for diabetic macular edema (DME) but are not universally effective. An automatic method that can predict whether a patient is likely to respond to anti-VEGF therapy can avoid unnecessary trial and error treatment strategies and promote the selection of more effective first-line therapies. The objective of this study is to automatically predict the efficacy of anti-VEGF treatment of DME in individual patients based on optical coherence tomography (OCT) images. We performed a retrospective study of 127 subjects treated for DME with three consecutive injections of ...

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      Mentions: Duke University

    17. Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome

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      Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome

      Purpose To validate the efficacy of a fully-automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2) Design Evaluation of diagnostic test or technology Participants 92 eyes from 62 participants with MacTel2 from a phase 2 clinical trial (NCT01949324) randomized to one of two treatment groups Methods The ellipsoid zone (EZ) defect areas were measured on spectral domain optical coherence tomography images of each eye at two time points (Baseline and Month 24) by a fully-automatic, deep learning-based segmentation algorithm. The change in EZ defect area ...

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      Mentions: Duke University

    18. Special Section Guest Editorial: Advances in Retinal Imaging

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      Special Section Guest Editorial: Advances in Retinal Imaging

      The guest editorial provides an introduction to the Special Section on Advanced Retinal Imaging: Instrumentation, Methods, and Applications. The retina is a peripheral part of the central nerve system (CNS) and shares many similarities with the cerebral cortex. They both have layered anatomy, the same types of functional elements and neurotransmitters, and similar vascular organization and blood-tissue barriers. With far fewer neuronal cell types and simpler anatomical structures, the retina is an excellent target for studying neural circuitry and neurovascular coupling. Meanwhile, approximately 80 percent of information from the outside world is processed as visual perception, 1 and retina-related blindness ...

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      Mentions: National Institutes of Health Duke University Stanford University

    19. Optical coherence refraction tomography

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      Optical coherence refraction tomography

      Optical coherence tomography (OCT) is a cross-sectional, micrometre-scale imaging modality with widespread clinical application. Typical OCT systems sacrifice lateral resolution to achieve long depths of focus for bulk tissue imaging, and therefore tend to have better axial than lateral resolution. Such anisotropic resolution can obscure fine ultrastructural features. Furthermore, conventional OCT suffers from refraction-induced image distortions. Here, we introduce optical coherence refraction tomography (OCRT), which extends the superior axial resolution to the lateral dimension, synthesizing undistorted cross-sectional image reconstructions from multiple conventional images acquired with angular diversity. In correcting refraction-induced distortions to register the OCT images, OCRT also achieves spatially ...

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      Mentions: Duke University

    20. Postdoctoral Associate Position at Duke Vision and Image Processing Laboratory

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      Postdoctoral Associate Position at Duke Vision and Image Processing Laboratory

      Immediate opening at Duke University Biomedical Engineering Department for a postdoc interested in interdisciplinary applications of image analysis and computer vision with emphasis on optical coherence tomography image analysis. Postdoc will be supervised by Prof. Sina Farsiu ( http://people.duke.edu/~sf59/ ). The candidate must have a demonstrated strong background in image processing as evidenced by publication of papers in high-impact peer-reviewed journals. Programming skill in MATLAB/Python is required. PhD in EE, BME, or CS. Prior experience in ophthalmic sciences, optical coherence tomography, adaptive optics is a plus but not necessary. Projects can be chosen based on the interests ...

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      Mentions: Duke University

    21. Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2

      Explore OSA | OSA Publishing Ophthalmology
      Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2

      Photoreceptor ellipsoid zone (EZ) defects visible on optical coherence tomography (OCT) are important imaging biomarkers for the onset and progression of macular diseases. As such, accurate quantification of EZ defects is paramount to monitor disease progression and treatment efficacy over time. We developed and trained a novel deep learning-based method called Deep OCT Atrophy Detection (DOCTAD) to automatically segment EZ defect areas by classifying 3-dimensional A-scan clusters as normal or defective. Furthermore, we introduce a longitudinal transfer learning paradigm in which the algorithm learns from segmentation errors on images obtained at one time point to segment subsequent images with higher ...

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      Mentions: Duke University

    22. Comparison of chorioretinal layers in rhesus macaques using spectral-domain optical coherence tomography and high-resolution histological sections

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      Comparison of chorioretinal layers in rhesus macaques using spectral-domain optical coherence tomography and high-resolution histological sections

      Nonhuman primates are important preclinical models of retinal diseases because they uniquely possess a macula similar to humans. Ocular imaging technologies such as spectral-domain optical coherence tomography (SD-OCT) allow noninvasive, in vivo measurements of chorioretinal layers with near-histological resolution. However, the boundaries are based on differences in reflectivity, and detailed correlations with histological tissue layers have not been explored in rhesus macaques, which are widely used for biomedical research. Here, we compare the macular anatomy and thickness measurements of chorioretinal layers in rhesus macaque eyes using SD-OCT and high-resolution histological sections. Images were obtained from methylmethacrylate-embedded histological sections of 6 ...

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      Mentions: Duke University UC Davis

    23. Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography

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      Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography

      Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization and measurement of retinal layers. To speed up quantitative analysis of disease biomarkers, an increasing number of automatic segmentation algorithms have been proposed to estimate the boundary locations of retinal layers. While the performance of these algorithms has significantly improved in recent years, a critical question to ask is how far we are from a theoretical limit to OCT segmentation performance. In this paper, we present the Cramèr-Rao lower bounds (CRLBs) for the problem of OCT layer segmentation. In deriving the CRLBs, we address the ...

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      Mentions: Duke University

    24. Characterization of Long Working Distance Optical Coherence Tomography for Imaging of Pediatric Retinal Pathology

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      Characterization of Long Working Distance Optical Coherence Tomography for Imaging of Pediatric Retinal Pathology

      Purpose : We determined the feasibility of fovea and optic nerve head imaging with a long working distance (LWD) swept source optical coherence tomography (OCT) prototype in adults, teenagers, and young children. Methods : A prototype swept source OCT system with a LWD (defined as distance from the last optical element of the imaging system to the eye) of 350 mm with custom fixation targets was developed to facilitate imaging of children. Imaging was performed in 49 participants from three age groups: 26 adults, 16 children 13 to 18 years old (teenagers), and seven children under 6 years old (young children) under ...

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      Mentions: Duke University
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