Sina Farsiu

Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes

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Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes Objective quantification of photoreceptor cell morphology, such as cell diameter and outer segment length, is crucial for early, accurate, and sensitive diagnosis and prognosis of retinal neurodegenerative diseases. Adaptive optics optical coherence tomography (AO-OCT) provides three-dimensional (3-D) visualization of photoreceptor cells in the living human eye. The current gold standard for extracting cell morphology from AO-OCT images involves the tedious process of 2-D manual marking. To automate this process and extend to 3-D analysis of the volumetric data, we propose a comprehensive deep learning framework to segment individual cone cells in AO-OCT scans. Our automated method achieved human-level performance in assessing cone photoreceptors of healthy and diseased participants captured with three different AO-OCT systems representing two different types of point scanning OCT: spectral domain and swept source.

Baseline Microperimetry and OCT in the RUSH2A Study: Structure-Function Association and Correlation with Disease Severity

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Purpose: To investigate baseline mesopic microperimetry (MP) and spectral domain optical coherence tomography (OCT) in the Rate of Progression in USH2A-related Retinal Degeneration (RUSH2A) study. Design: Natural history study SETTING: 16 clinical sites in Europe and North America STUDY POPULATION: Participants with Usher syndrome type 2 (USH2) (N=80) or autosomal recessive nonsyndromic RP (ARRP) (N=47) associated with biallelic disease-causing sequence variants in USH2A. Observation procedures: General linear models were used to assess characteristics including disease duration, MP mean sensitivity and OCT intact ellipsoid zone (EZ) area. The associations between mean sensitivity and EZ area with other measures, including best corrected visual acuity (BCVA) and central subfield thickness (CST) within the central 1 mm, were assessed using Spearman correlation coefficients. Main outcome measures: Mean sensitivity on MP; EZ area and CST on OCT RESULTS: All participants (N=127) had OCT...

Computational 3D microscopy with optical coherence refraction tomography

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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 a resolution-enhanced, speckle-reduced, refraction-corrected 3D reconstruction. Our label-free computational 3D microscope features a novel optical design incorporating a parabolic mirror to enable the capture of 5D plenoptic datasets, consisting of millimetric 3D f...

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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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, median) DSC was 0.79 ± 0.27, 0.90. The average absolute difference in total EZ area was 0.62 ± 1.41, 0.22 mm2 with a correlation of 0.97. The average absolute difference in the maximum EZ length was 222 ± 288, 126 μm wi...

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

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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 the SC lumen. The proposed model achieved a mean Dice Similarity Coefficient (DSC) of 0.694 ± 0.256 and median DSC of 0.791, while manual segmentation performed by a second expert grader achieved a mean and median DSC of 0.713 ± 0.209 and 0.763, respectively. This work presents the first automatic met...

Optical Coherence Refraction Tomography

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Combining principles of computed tomography with modern machine-learning tools significantly improves OCT’s resolution while extending imaging depth, reducing noise and reconstructing refractive-index maps of biological samples. ( Read Full Article )

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

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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 the epithelium in in vivo human esophageal OCT images. ( Read Full Article )

Unified k-space theory of optical coherence tomography

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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, dispersion, aberration, limited depth of focus, and speckle. The theory also unifies diffraction tomography, confocal microscopy, point-scanning OCT, line-field OCT, full-field OCT, Bessel beam OCT, transillumination OCT, interferometric synthetic aperture microscopy (ISAM), and optical coherence refraction tomography (OCRT), among others. Our unified theory no...

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

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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 MIOCT were compared to the intended injection volume. Results: Validation of image-based volume measurements of ceramic spheres showed accuracy to ±0.029 µL (5.6%) for objects imaged over the posterior pole and ±0.025 µL (4.8%) over peripheral retina. The mean expected inje...

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

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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 MIOCT were compared to the intended injection volume. Results: Validation of image-based volume measurements of ceramic spheres showed accuracy to ±0.029 µL (5.6%) for objects imaged over the posterior pole and ±0.025 µL (4.8%) over peripheral retina. The mean expected inje...

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

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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 the training process. Experimental results show that WeakGCSeg is on par with or superior to human experts and is superior to other state-of-the-art networks. The automated quantitative features of individual GCLs show an increase in structure–fun...

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

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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 the Duke OCT Retinal Analysis Program. Results: The sizes of 213 individual drusen were measured on CFP and SD-OCT . The drusen diameter measured on CFP was significantly correlated with those determined on SD-OCT (R:0.879, p <0.001). Based on the corresponding formula: drusen diameter on SD-OCT =0.77*( drusen di...

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

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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 seven. The area of new onset GA and one size-matched control region in the same eye were separately segmented and corresponding spatial volumes on registered SDOCT images at the GA incident year, and at two, three, and four years prior were defined. Differen...

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

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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 the second stage, CNN2 segmented the retinal cavitations in a B-scan. We evaluated the performance of the proposed method against alternative methods using several performance metrics and manual segmentations as the gold standard. Results: The proposed method was computationally efficient and accurate...

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