OCTID: Optical Coherence Tomography Image Database

Journal of Biomedical Optics, 2010

We demonstrate quantitative analysis and error correction of optical coherence tomography ͑OCT͒ retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT ͑Carl Zeiss Meditec, Dublin, California͒ B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 m and 26.71 m when using the inner segment/outer segment ͑IS/OS͒ junction and outer segment/retinal pigment epithelium ͑OS/RPE͒ junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis ͑OCTRIMA͒ algorithm is between 0.27 and 1.47 m and 0.6 and 1.76 m for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R 2 Ͼ 0.98 for all Early Treatment Diabetic Retinopathy Study ͑ETDRS͒ regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

2021

Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called “Livelayer” which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra’s Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, deman...

Ophthalmology, 2006

Objective-To assess high-speed ultrahigh-resolution optical coherence tomography (OCT) image resolution, acquisition speed, image quality, and retinal coverage for the visualization of macular pathologies.

Over the 15 years since the original description, optical coherence tomography (OCT) has become one of the key diagnostic technologies in the ophthalmic subspecialty areas of retinal diseases and glaucoma. The reason for the widespread adoption of this technology originates from at least two properties of the OCT results: on the one hand, the results are accessible to the non-specialist where microscopic retinal abnormalities are grossly and easily noticeable; on the other hand, results are reproducible and exceedingly quantitative in the hands of the specialist. However, as in any other imaging technique in ophthalmology, some artifacts are expected to occur. Understanding of the basic principles of image acquisition and data processing as well as recognition of OCT limitations are crucial issues to using this equipment with cleverness. Herein, we took a brief look in the past of OCT and have explained the key basic physical principles of this imaging technology. In addition, each of the several steps encompassing a third generation OCT evaluation of retinal tissues has been addressed in details. A comprehensive explanation about next generation OCT systems has also been provided and, to conclude, we have commented on the future directions of this exceptional technique.

The four major causes of blindness are age-related diseases, out of which three affects the retina. These diseases, i.e., glaucoma, diabetic retinopathy, and age-related macular degeneration, require lifelong treatment and cause irreversible blindness. Conversely, early diagnosis has been shown to curtail or prevent blindness and visual impairments. A critical element of the clinical diagnosis is the analysis of individual retinal layer properties, as the manifestation of the dominant eye diseases has been shown to correlate with structural changes to the retinal layers. Regrettably, manual segmentation is dependent on the ophthalmologist's level of expertise, and currently becoming impractical due to advancement in imaging modalities. Inherently, much research on computer-aided diagnostic methods is conducted to aid in extracting useful layer information from these images, which were inaccessible without these techniques. However, speckle noise and intensity inhomogeneity remain a challenge with a detrimental effect on the performance of automated methods. In this paper, we propose a method comprising of fuzzy image processing techniques and graph-cut methods to robustly segment optical coherence tomography (OCT) into five (5) distinct layers. Notably, the method establishes a specific region of interest to suppress the interference of speckle noise, while Fuzzy C-means is utilized to build data terms for better integration into the continuous max-flow to handle inhomogeneity. The method is evaluated on 225 OCT B-scan images, and promising experimental results were achieved. The method will allow for early diagnosis of major eye diseases by providing the basic, yet critical layer information necessary for an effective eye examination. INDEX TERMS Medical image analysis, optical coherence tomography, fuzzy image processing, graph-cut, continuous max-flow.

Ophthalmology, 2005

Objective-To compare ultrahigh-resolution optical coherence tomography (UHR OCT) with standard-resolution OCT for imaging macular diseases, develop baselines for interpreting OCT images, and identify situations where UHR OCT can provide additional information on disease morphology.

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

PloS one, 2016

Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) for automated intra-retinal layer segmentation and image scaling for three OCT systems. Healthy participants (n = 25) underwent macular volume scans using a Cirrus HD-OCT (Zeiss), 3D-OCT 1000 (Topcon), and a non-commercial long-wavelength (1040nm) OCT on two occasions. Mean thickness of 10 intra-retinal layers was measured in three ETDRS subfields (fovea, inner ring and outer ring) using the Iowa Reference Algorithms. Where available, total retinal thicknesses were measured using on-board software. Measured axial eye length (AEL)-dependent scaling was used throughout, with a comparison made to the system-specific fixed-AEL scaling. Inter-session repeatability and...

Archives of Ophthalmology, 2003

Objectives: To demonstrate a new generation of ophthalmic optical coherence tomography (OCT) technology with unprecedented axial resolution for enhanced imaging of intraretinal microstructures and to investigate its clinical feasibility to visualize intraretinal morphology of macular pathology.

Retina (Philadelphia, Pa.), 2017

To characterize inner retinal damage in patients with dry age-related macular degeneration (AMD) using high-resolution spectral domain optical coherence tomography images. Sixty eyes of 60 patients with AMD were categorized using the Age-Related Eye Disease Study (AREDS) severity scale. Spectral domain optical coherence tomography images of these patients were quantified by manually correcting the segmentation of each retinal layer, including the retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer to ensure accurate delineation of layers. The mean ganglion cell complex thickness values (ganglion cell layer + inner plexiform layer + retinal nerve fiber layer) were compared with 30 eyes of 30 healthy subjects. Ninety percent of eyes (81 eyes) required manual correction of segmentation. Compared with healthy subjects, mean ganglion cell complex thicknesses significantly decreased in more advanced dry AMD eyes, and this decrease was predominantly related to a chang...

Investigative Ophthalmology & Visual Science, 2008

Purpose-To visualize, quantitatively assess, and interpret outer retinal morphology by using highspeed, ultrahigh-resolution (UHR) OCT.

Ophthalmology, 2004

Objective-To compare ultrahigh-resolution optical coherence tomography (UHR OCT) with standard-resolution OCT for imaging macular diseases, develop baselines for interpreting OCT images, and identify situations where UHR OCT can provide additional information on disease morphology.

2011

Optical coherence tomography (OCT) is an important mode of biomedical imaging for the diagnosis and management of ocular disease. Here we report on the construction of a synthetic retinal OCT image data set that may be used for quantitative analysis of image processing methods. Synthetic image data were generated from statistical characteristics of real images (n = 14). Features include: multiple stratified layers with representative thickness, boundary gradients, contour, and intensity distributions derived from real data. The synthetic data also include retinal vasculature with typical signal obscuration beneath vessels. This synthetic retinal image can provide a realistic simulated data set to help quantify the performance of image processing algorithms.

2020

The Ophthalmology Science has recently witnessed marked progress due to the advent of divergent imaging techniques, especially Optical Coherence Tomography which has caught many physicians' attention for being exact, rapid, non-invasive and low-cost. In this paper, we wish to solve the difficulties associated with OCT image segmentation by offering a handy software written in the MATLAB App Designer environment which helps researchers and clinicians to easily segment the images, save the numerical outcomes and send them for proper analysis. Serving an unambiguous user interface along with a unified platform in which all necessary functions have been incorporated graphically has made this software so unique that it could be recommended to anyone tending to work on ocular OCT layers and fluids segmentation.