Quantitative Analysis of Radial Peripapillary Capillary Network in Patients With Papilledema Compared with Healthy Subjects Using Optical Coherence Tomography Angiography

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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Quantitative analysis of choriocapillaris in non-human primates using swept-source optical coherence tomography angiography (SS-OCTA)

Biomedical Optics Express ◽

10.1364/boe.10.000356 ◽

2018 ◽

Vol 10 (1) ◽

pp. 356 ◽

Cited By ~ 8

Author(s):

Bingyao Tan ◽

Jacqueline Chua ◽

Veluchamy Amutha Barathi ◽

Mani Baskaran ◽

Anita Chan ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Quantitative Analysis ◽

Optical Coherence Tomography Angiography ◽

Optical Coherence ◽

Swept Source

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Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545822420019 ◽

2021 ◽

Author(s):

Jian Liu ◽

Shixin Yan ◽

Nan Lu ◽

Dongni Yang ◽

Chunhui Fan ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Healthy Subjects ◽

Optical Coherence Tomography Angiography ◽

Automatic Segmentation ◽

Qualitative Evaluation ◽

Foveal Avascular Zone ◽

Watershed Algorithm ◽

Strong Positive Correlation ◽

Optical Coherence ◽

Avascular Zone

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, “over-segmentation” is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant “dams”. This paper analyzed the relationship between the “dams” length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of “over-segmentation”. Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of “perfect segmentation” (score of 3) and “good segmentation” (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases.

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Optical coherence tomography angiography in myopic peripapillary intrachoroidal cavitation complicated by choroidal neovascularization

European Journal of Ophthalmology ◽

10.1177/1120672120944016 ◽

2020 ◽

pp. 112067212094401

Author(s):

Chiara Comune ◽

Daniela Montorio ◽

Gilda Cennamo

Keyword(s):

Optical Coherence Tomography ◽

Choroidal Neovascularization ◽

Optical Coherence Tomography Angiography ◽

Vascular Density ◽

Optical Coherence ◽

Pathological Myopia ◽

Group 2 ◽

The University ◽

Radial Peripapillary Capillary ◽

Intrachoroidal Cavitation

Purpose: To detect the vessel density (VD) of the radial peripapillary capillary (RPC) in eyes affected by pathological myopia with or without a peripapillary intrachoroidal cavitation (PICC) and in eyes with PICC complicated by choroidal neovascularization (CNV), using optical coherence tomography angiography (OCTA). Methods: We prospectively enrolled highly myopic patients from January 2016 to December 2019 at the Eye Clinic of the University of Naples “Federico II.” We divided included patients into three groups: group 1 including patients with PICC complicated by CNV; group 2 including patients with PICC without complications; group 3 including patients with high myopia without PICC and CNV. One-way analysis of variance (ANOVA) followed by Bonferroni post hoc analysis was used to evaluate differences in VD of radial peripapillary capillary (RPC) in papillary whole, peripapillary regions and its sectors among the three groups. Results: We enrolled 12 highly myopic eyes with PICC complicated by CNV, 21 highly myopic eyes with PICC without CNV and 23 highly myopic eyes without PICC. The myopic eyes with PICC revealed a statistically significant reduction in VD of the RPC comparing to the other groups ( p < 0.001), especially in eyes affected by myopic PICC complicated by CNV ( p < 0.001). These results were similar analyzing the VD in different sectors of the peripapillary region among the three groups ( p < 0.001). Conclusion: OCTA detects the changes in peripapillary vascular density of highly myopic eyes. We demonstrated that the RPC vasculature is significantly influenced by the presence of PICC, especially in myopic eyes developing a CNV.

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