Optical coherence tomography
Vasudevan lakshminarayanan2015, Journal of Modern Optics
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Livelayer: A Semi-Automatic Software for Segmentation of Layers and Objects in Optical Coherence Tomography Images
2020
The Ophthalmology Science has recently witnessed marked progress due to the advent of divergent imaging techniques, especially Optical Coherence Tomography (OCT) which has caught many physicians' attention for being exact, rapid, non-invasive and low-cost. Given these interesting features of OCTs as well as the capacity 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 felt more than ever before. In this paper, we wish to address this need and 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 ...
A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina
Journal of Medical Signals & Sensors, 2013
Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states, and molecular content. [1] In contrast to OCT technology development which has been a field of active research since 1991, OCT image segmentation has only been more fully explored during the last decade. Segmentation, however, remains one of the most difficult and at the same time most commonly required steps in OCT image analysis. No typical segmentation method exists that can be expected to work equally well for all tasks. [2] One of the most challenging problems in OCT image segmentation is designing a system to work properly in clinical applications. There is no doubt that algorithms and research projects work on a limited number of images with some determinate abnormalities (or even on normal subjects) and such limitations make them more appropriate for bench and not for the bedside. Moreover, OCT images are inherently noisy, thus often requiring the utilization of 3D contextual information. Furthermore, the structure of the A b s t r A c t Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging.
A Semi-Automatic Software for Segmentation and Feature Extraction from Optical Coherence Tomography Images (Semi SFE)
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.
Artifacts in Automatic Retinal Segmentation Using Different Optical Coherence Tomography Instruments
Retina-the Journal of Retinal and Vitreous Diseases, 2010
The purpose of this study was to compare and evaluate artifact errors in automatic inner and outer retinal boundary detection produced by different time-domain and spectral-domain optical coherence tomography (OCT) instruments. Methods: Normal and pathologic eyes were imaged by six different OCT devices. For each instrument, standard analysis protocols were used for macular thickness evaluation. Error frequencies, defined as the percentage of examinations affected by at least one error in retinal segmentation (EF-exam) and the percentage of total errors per total B-scans, were assessed for each instrument. In addition, inner versus outer retinal boundary delimitation and central (1,000 m) versus noncentral location of errors were studied. Results: The study population of the EF-exam for all instruments was 25.8%. The EF-exam of normal eyes was 6.9%, whereas in all pathologic eyes, it was 32.7% (P Ͻ 0.0001). The EF-exam was highest in eyes with macular holes, 83.3%, followed by epiretinal membrane with cystoid macular edema, 66.6%, and neovascular age-related macular degeneration, 50.3%. The different OCT instruments produced different EF-exam values (P Ͻ 0.0001). The Zeiss Stratus produced the highest percentage of total errors per total B-scans compared with the other OCT systems, and this was statistically significant for all devices (P Յ 0.005) except the Optovue RTvue-100 (P ϭ 0.165). Conclusion: Spectral-domain OCT instruments reduce, but do not eliminate, errors in retinal segmentation. Moreover, accurate segmentation is lower in pathologic eyes compared with normal eyes for all instruments. The important differences in EF among the instruments studied are probably attributable to analysis algorithms used to set retinal inner and outer boundaries. Manual adjustments of retinal segmentations could reduce errors, but it will be important to evaluate interoperator variability.
Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis
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.
Performance evaluation of automated segmentation software on optical coherence tomography volume data
Journal of Biophotonics, 2016
Over the past two decades a significant number of OCT segmentation approaches have been proposed in the literature. Each methodology has been conceived for and/or evaluated using specific datasets that do not reflect the complexities of the majority of widely available retinal features observed in clinical settings. In addition, there does not exist an appropriate OCT dataset with ground truth that reflects the realities of everyday retinal features observed in clinical settings. While the need for unbiased performance evaluation of automated segmentation algorithms is obvious, the validation process of segmentation algorithms have been usually performed by comparing with manual labelings from each study and there has been a lack of common ground truth. Therefore, a performance comparison of different algorithms using the same ground truth has never been performed. This paper reviews research‐oriented tools for automated segmentation of the retinal tissue on OCT images. It also eval...
Automated segmentation of intramacular layers in Fourier domain optical coherence tomography structural images from normal subjects
Journal of Biomedical Optics, 2012
Segmentation of optical coherence tomography (OCT) cross-sectional structural images is important for assisting ophthalmologists in clinical decision making in terms of both diagnosis and treatment. We present an automatic approach for segmenting intramacular layers in Fourier domain optical coherence tomography (FD-OCT) images using a searching strategy based on locally weighted gradient extrema, coupled with an error-removing technique based on statistical error estimation. A two-step denoising preprocess in different directions is also employed to suppress random speckle noise while preserving the layer boundary as intact as possible. The algorithms are tested on the FD-OCT volume images obtained from four normal subjects, which successfully identify the boundaries of seven physiological layers, consistent with the results based on manual determination of macular OCT images.
Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook
Applied Sciences, 2021
Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning.
Retinal assessment using optical coherence tomography
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.
Mathematical analysis of texture indicators for the segmentation of optical coherence tomography images
Optik, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Retinal Layer Segmentation in Optical Coherence Tomography Images
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.
Optical Coherence Tomography : Newer Techniques , Newer Machines
2015
Introduction Since 1991, when optical coherence tomography (OCT) was first introduced as a tool for investigation in ophthalmology, OCT has become the mainstay in retinal imaging and, in some situations, has supplanted fundus fluoresce in angiography which is regarded, even today, as the gold standard for investigations in choroidal and retinal disorders. By definition, OCT represents a non-invasive method for cross-sectional imaging of the internal retinal structures through the detection of optical reflections and echo time delays of light, using low coherence interferometry to subsequently produce in vivo two-dimensional images of internal tissue microstructure. Initially, the time domain (TD) technology was used (Stratus OCT, Carl Zeiss Meditec), which employed a mobile reference arm mirror that sequentially measured light echoes from time delays with an acquisition speed of 400 A scans/ second and axial resolution of 810 μm. TD OCT was limited by longer acquisition times (due t...
OCTID: Optical coherence tomography image database
Computers & Electrical Engineering, 2020
Optical coherence tomography (OCT) is a non-invasive imaging modality which is widely used in clinical ophthalmology. OCT images are capable of visualizing deep retinal layers which is crucial for early diagnosis of retinal diseases. In this paper, we describe a comprehensive open-access database containing more than 500 highresolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The images were obtained from a raster scan protocol with a 2mm scan length and 512x1024 pixel resolution. We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation. In addition, we have provided a user friendly GUI which can be used by clinicians for manual (and semi-automated) segmentation.
Automated segmentation of tissue structures in optical coherence tomography data
Journal of Biomedical Optics, 2009
Segmentation of optical coherence tomography ͑OCT͒ images provides useful information, especially in medical imaging applications. Because OCT images are subject to speckle noise, the identification of structures is complicated. Addressing this issue, two methods for the automated segmentation of arbitrary structures in OCT images are proposed. The methods perform a seeded region growing, applying a model-based analysis of OCT A-scans for the seed's acquisition. The segmentation therefore avoids any userintervention dependency. The first region-growing algorithm uses an adaptive neighborhood homogeneity criterion based on a model of an OCT intensity course in tissue and a model of speckle noise corruption. It can be applied to an unfiltered OCT image. The second performs region growing on a filtered OCT image applying the local median as a measure for homogeneity in the region. Performance is compared through the quantitative evaluation of artificial data, showing the capabilities of both in terms of structures detected and leakage. The proposed methods were tested on real OCT data in different scenarios and showed promising results for their application in OCT imaging.
Automated segmentation of the macula by optical coherence tomography
Optics Express, 2009
This paper presents optical coherence tomography (OCT) signal intensity variation based segmentation algorithms for retinal layer identification. Its main ambition is to reduce the calculation time required by layer identification algorithms. Two algorithms, one for the identification of the internal limiting membrane (ILM) and the other for retinal pigment epithelium (RPE) identification are implemented to evaluate structural features of the retina. Using a 830 nm spectral domain OCT device, this paper demonstrates a segmentation method for the study of healthy and diseased eyes.
New Directions in Ophthalmic Optical Coherence Tomography
Optometry and Vision Science, 2012
The rapid development of optical coherence tomography (OCT) and its ophthalmic applications has resulted in the emergence of new laboratory and commercial systems that vary in performance and functionality. The introduction of high-speed imaging capabilities has abrogated the primary limitation of early OCT technology by providing in vivo three-dimensional volumetric reconstructions of both anterior and posterior segments of the human eye within reasonable time constraints. Currently, high-speed swept source OCT technology has made it possible to achieve OCT acquisition speeds of several million A-scans/s. Another direction of OCT development includes the introduction of adaptive optics to imaging of the posterior segment of the eye that allows correction of the eye's static and dynamic aberrations, resulting in the achievement of volumetric cellular resolution retinal imaging. In this review, we introduce readers to various aspects of the development of OCT technology within the context of its ophthalmic applications. We point out directions for future development and indicate different perspectives on this dynamically expanding method. We give a few examples of how OCT has been used over the past few years and describe how high-speed OCT imaging may be used in the future in clinical practice. (Optom Vis Sci 2012;89:524-542)
Cellular Level Imaging of the Retina Using Optical Coherence Tomography
Selected Topics in Optical Coherence Tomography, 2012
Characterization of Outer Retinal Morphology with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography
Investigative Ophthalmology & Visual Science, 2008
Purpose-To visualize, quantitatively assess, and interpret outer retinal morphology by using highspeed, ultrahigh-resolution (UHR) OCT.
Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
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...
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