Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images

2018 ◽  
Vol 45 (10) ◽  
pp. 4582-4599 ◽  
Author(s):  
Nabila Eladawi ◽  
Mohammed Elmogy ◽  
Fahmi Khalifa ◽  
Mohammed Ghazal ◽  
Nicola Ghazi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence ◽  
Retinal Blood Vessel

Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study

2018 ◽  
Vol 102 (11) ◽  
pp. 1564-1569 ◽  
Author(s):  
Harpal Singh Sandhu ◽  
Nabila Eladawi ◽  
Mohammed Elmogy ◽  
Robert Keynton ◽  
Omar Helmy ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Optical Coherence Tomography Angiography ◽  
Area Under The Curve ◽  
Foveal Avascular Zone ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Cad System

BackgroundOptical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images.MethodsThis was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features—blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)—were segmented from these images and used to train a new, automated classifier.ResultsOne hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%.ConclusionAutomated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.

Retinal Blood Vessel Caliber Estimation for Optical Coherence Tomography Angiography Images Based on 3D Superellipsoid Modeling

2019 ◽  
Vol 19 (02) ◽  
pp. 1950011 ◽  
Author(s):  
Myounghee Han ◽  
Yongjoo Kim ◽  
Jang Ryul Park ◽  
Benjamin J. Vakoc ◽  
Wang-Yuhl Oh ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Blood Vessel ◽  
Optical Coherence Tomography Angiography ◽  
3D Visualization ◽  
Retinal Vessel ◽  
Flow Speed ◽  
Retinal Diseases ◽  
Optical Coherence ◽  
Retinal Blood Vessel

Changes of retinal blood vessel calibers may reflect various retinal diseases and even several non-retinal diseases. We propose a new method to estimate retinal vessel calibers from 3D optical coherence tomography angiography (OCTA) images based on 3D modeling using superellipsoids. Taking advantage of 3D visualization of the retinal tissue microstructures in vivo provided by OCTA, our method can detect retinal blood vessels precisely, estimate their calibers reliably, and show the relative flow speed visually.

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.

Multiscale correlation of microvascular changes on optical coherence tomography angiography with retinal sensitivity in diabetic retinopathy

Retina ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Emily S. Levine ◽  
Eric M. Moult ◽  
Eugenia Custo Greig ◽  
Yi Zhao ◽  
Varsha Pramil ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Retinal Sensitivity ◽  
Optical Coherence ◽  
Microvascular Changes

Detecting papillary neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography

2016 ◽  
Vol 96 (3) ◽  
pp. 321-323 ◽  
Author(s):  
Maria Cristina Savastano ◽  
Matteo Federici ◽  
Benedetto Falsini ◽  
Aldo Caporossi ◽  
Angelo Maria Minnella
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence

scholarly journals Parafoveal Nonperfusion Analysis in Diabetic Retinopathy Using Optical Coherence Tomography Angiography

2018 ◽  
Vol 7 (4) ◽  
pp. 4 ◽  
Author(s):  
Brian D. Krawitz ◽  
Erika Phillips ◽  
Richard D. Bavier ◽  
Shelley Mo ◽  
Joseph Carroll ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence

Quantitative evaluation of optical coherence tomography angiography images of diabetic retinopathy eyes before and after removal of projection artifacts

2018 ◽  
Vol 11 (9) ◽  
pp. e201800003 ◽  
Author(s):  
Nivedhitha Govindaswamy ◽  
Santosh G. Gadde ◽  
Lavanya Chidambara ◽  
Devanshi Bhanushali ◽  
Neha Anegondi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Quantitative Evaluation ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence ◽  
Before And After
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