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Optical coherence tomography

About: Optical coherence tomography is a research topic. Over the lifetime, 19051 publications have been published within this topic receiving 477433 citations. The topic is also known as: optical coherent tomography.


Papers
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Journal ArticleDOI
TL;DR: The machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images, which should help to improve the diagnostic accuracy inglaucoma.
Abstract: This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

109 citations

Journal ArticleDOI
TL;DR: This work presents a numerical algorithm for computationally correcting the effect of material dispersion on OCT reflectance data for homogeneous and stratified media with broad spectral bandwidths and highly dispersive media or thick objects.
Abstract: The resolution of optical coherence tomography (OCT) often suffers from blurring caused by material dispersion. We present a numerical algorithm for computationally correcting the effect of material dispersion on OCT reflectance data for homogeneous and stratified media. This is experimentally demonstrated by correcting the image of a polydimethyl siloxane microfludic structure and of glass slides. The algorithm can be implemented using the fast Fourier transform. With broad spectral bandwidths and highly dispersive media or thick objects, dispersion correction becomes increasingly important.

109 citations

Patent
13 Mar 2007
TL;DR: In this article, various methods for mapping optical coherence tomography (OCT) data to facilitate review and diagnosis are disclosed, including high resolution 2D line scans along with lower density 3D cube scans and displayed in a manner to provide context to the clinician.
Abstract: Various methods are disclosed for mapping optical coherence tomography (OCT) data to facilitate review and diagnosis. In one aspect, high resolution 2D line scans are obtained along with lower density 3D cube scans and displayed in a manner to provide context to the clinician. In another aspect, OCT data is analyzed to provide information about non-uniformities of the tissue. Binary image maps of maps useful for determining tautness of membranes are also disclosed.

109 citations

Journal ArticleDOI
TL;DR: OCTA with motion tracking through an auxiliary real-time line scan ophthalmoscope is reported that is clinically feasible to image functional retinal vasculature in patients, with a coverage of more than 60 degrees of retina while still maintaining high definition and resolution.
Abstract: Optical coherence tomography angiography (OCTA) allows for the evaluation of functional retinal vascular networks without a need for contrast dyes. For sophisticated monitoring and diagnosis of retinal diseases, OCTA capable of providing wide-field and high definition images of retinal vasculature in a single image is desirable. We report OCTA with motion tracking through an auxiliary real-time line scan ophthalmoscope that is clinically feasible to image functional retinal vasculature in patients, with a coverage of more than 60 degrees of retina while still maintaining high definition and resolution. We demonstrate six illustrative cases with unprecedented details of vascular involvement in retinal diseases. In each case, OCTA yields images of the normal and diseased microvasculature at all levels of the retina, with higher resolution than observed with fluorescein angiography. Wide-field OCTA technology will be an important next step in augmenting the utility of OCT technology in clinical practice.

109 citations

Journal ArticleDOI
TL;DR: Ultrahigh-resolution OCT provides high-resolution images of the ocular posterior segment, which improves the ability to detect retinal abnormalities due to glaucoma.

109 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,805
20223,557
2021907
20201,074
20191,127
20181,113