scholarly journals Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

2017 ◽  
Vol 8 (12) ◽  
pp. 5384 ◽  
Author(s):  
Zhuo Wang ◽  
Acner Camino ◽  
Miao Zhang ◽  
Jie Wang ◽  
Thomas S. Hwang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Automated Detection ◽  
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 Alterations in Retinal Layer Thickness and Reflectance at Different Stages of Diabetic Retinopathy by En Face Optical Coherence Tomography

2016 ◽  
Vol 57 (9) ◽  
pp. OCT341 ◽  
Author(s):  
Justin Wanek ◽  
Norman P. Blair ◽  
Felix Y. Chau ◽  
Jennifer I. Lim ◽  
Yannek I. Leiderman ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Layer Thickness ◽  
Retinal Layer ◽  
Optical Coherence ◽  
En Face

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
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