Deep learning image analysis of optical coherence tomography angiography measured vessel density improves classification of healthy and glaucoma eyes

2021 ◽  
Author(s):  
Christopher Bowd ◽  
Akram Belghith ◽  
Linda M. Zangwill ◽  
Mark Christopher ◽  
Michael H. Goldbaum ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Deep Learning ◽  
Optical Coherence Tomography Angiography ◽  
Vessel Density ◽  
Optical Coherence

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

scholarly journals The Role of Optical Coherence Tomography Angiography in Moderate and Advanced Primary Open-Angle Glaucoma

2021 ◽  
Author(s):  
Yadollah Eslami ◽  
Sepideh Ghods ◽  
Massood Mohammadi ◽  
Mona Safizadeh ◽  
Ghasem Fakhraie ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optical Coherence Tomography Angiography ◽  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Vessel Density ◽  
Mean Deviation ◽  
Optical Coherence ◽  
Open Angle ◽  
The Mean ◽  
Inferior Quadrant

Abstract Purpose: To evaluate the relationship between structure and function in moderate and advanced primary open-angle glaucoma (POAG) and to determine the accuracy of structure and vasculature for discriminating moderate from advanced POAG.Methods: In this cross-sectional study 25 eyes with moderate and 40 eyes with advanced POAG were enrolled. All eyes underwent measurement of the thickness of circumpapillary retinal nerve fiber layer (cpRNFL) and macular ganglion cell complex (GCC), and optical coherence tomography angiography (OCTA) of the optic nerve head (ONH) and macula. Visual field (VF) was evaluated by Swedish interactive threshold algorithm and 24-2 and 10-2 patterns. The correlation between structure and vasculature and the mean deviation (MD) of the VFs was evaluated by a partial correlation coefficient. The area under the receiver operating characteristic curve (AUC) was applied for assessing the power of variables for discrimination moderate from advanced POAG.Results: Superior cpRNFL, superior GCC, whole image vessel density (wiVD) of the ONH area, and vessel density in inferior quadrant of perifovea had the strongest correlation with the mean deviation (MD) of the VF 24-2 (r= .351, .558, .649 and .397; p< .05). The greatest AUCs belonged to inferior cpRNFL (.789), superior GCC (.818), vessel density of the inferior hemifield of ONH area (.886), and vessel density in inferior quadrant of perifovea (.833) without statistically significant difference in pairwise comparison.Conclusion: Vasculature has a stronger correlation than the structure with MD in moderate and advanced POAG and is as accurate as structure in discrimination moderate from advanced POAG.

OCTAI: Smartphone-based Optical Coherence Tomography Image Analysis System

2021 ◽  
Author(s):  
Adrit Rao ◽  
Harvey A. Fishman
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Deep Learning ◽  
Early Stage ◽  
Real Life ◽  
Learning System ◽  
Optical Coherence Tomography Image ◽  
Image Analysis System ◽  
Optical Coherence ◽  
Oct Image

Identifying diseases in Optical Coherence Tomography (OCT) images using Deep Learning models and methods is emerging as a powerful technique to enhance clinical diagnosis. Identifying macular diseases in the eye at an early stage and preventing misdiagnosis is crucial. The current methods developed for OCT image analysis have not yet been integrated into an accessible form-factor that can be utilized in a real-life scenario by Ophthalmologists. Additionally, current methods do not employ robust multiple metric feedback. This paper proposes a highly accurate smartphone-based Deep Learning system, OCTAI, that allows a user to take an OCT picture and receive real-time feedback through on-device inference. OCTAI analyzes the input OCT image in three different ways: (1) full image analysis, (2) quadrant based analysis, and (3) disease detection based analysis. With these three analysis methods, along with an Ophthalmologist's interpretation, a robust diagnosis can potentially be made. The ultimate goal of OCTAI is to assist Ophthalmologists in making a diagnosis through a digital second opinion and enabling them to cross-check their diagnosis before making a decision based on purely manual analysis of OCT images. OCTAI has the potential to allow Ophthalmologists to improve their diagnosis and may reduce misdiagnosis rates, leading to faster treatment of diseases.

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