Optical Coherence Tomography Angiography Projection Artifact Removal: Impact on Capillary Density and Interaction with Diabetic Retinopathy Severity

Purpose. To analyze foveal microvascular abnormalities in different stages of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) with projection artifact removal (PAR). Methods. We analyzed 93 eyes of 59 patients with diabetes—31 with no DR (no DR), 34 with mild to moderate nonproliferative DR (mild DR), and 28 with severe nonproliferative DR to proliferative DR (severe DR)—and 31 age-matched healthy controls. Sections measuring 3 × 3 mm2 centered on the fovea were obtained using OCTA. The area, perimeter, and acircularity index (AI) of the foveal avascular zone (FAZ), vessel density within a 300 μm wide region of the FAZ (FD-300), and parafoveal vessel density in the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were calculated using novel built-in software with PAR. Results. There was no statistically significant difference in the FAZ area (p=0.162). There was a statistically significant difference in the FAZ perimeter (p=0.010) and the AI (p<0.001) between the four groups. There was a correlation between the AI and the increasing severity of DR (p=0.010). Statistically significant decreases of vessel density in the FD-300, SCP, and DCP were observed (all p<0.001). There was a difference in parafoveal vessel density in the DCP between the healthy control eyes and the eyes with diabetes without DR (p=0.027). There was a significant correlation between vessel density and increasing severity of DR (p<0.001). Conclusion. Compared with the FAZ area, AI allows a more helpful quantitative assessment of the changes in the FAZ. Vessel density determined using OCTA with PAR might be a useful parameter indicating the progression of DR. Parafoveal vessel density in the DCP after PAR might be a potential early biomarker of DR before appearance of clinically evident retinopathy and needs further investigation.

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Three Different Optical Coherence Tomography Angiography Measurement Methods for Assessing Capillary Density Changes in Diabetic Retinopathy

Ophthalmic Surgery Lasers and Imaging Retina ◽

10.3928/23258160-20170428-03 ◽

2017 ◽

Vol 48 (5) ◽

pp. 378-384 ◽

Cited By ~ 15

Author(s):

Alexandre Pedinielli ◽

Sophie Bonnin ◽

Mohamed El Sanharawi ◽

Valérie Mané ◽

Ali Erginay ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Diabetic Retinopathy ◽

Optical Coherence Tomography Angiography ◽

Capillary Density ◽

Measurement Methods ◽

Optical Coherence

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

Scientific Reports ◽

10.1038/s41598-020-80058-x ◽

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.

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