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.

Optical coherence tomography angiography in diabetic patients without diabetic retinopathy

2019 ◽  
Vol 30 (6) ◽  
pp. 1418-1423 ◽  
Author(s):  
Claudio Furino ◽  
Grazia Montrone ◽  
Maria Vittoria Cicinelli ◽  
Stefania Balestra ◽  
Maria Oliva Grassi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Vessel Density ◽  
Foveal Avascular Zone ◽  
Diabetic Patients ◽  
Optical Coherence ◽  
Avascular Zone ◽  
Capillary Plexus ◽  
Superficial Capillary Plexus

Purpose: To investigate a subset of diabetic patients without diabetic retinopathy with optical coherence tomography angiography, assessing the differences in macular perfusion between diseased eyes and healthy controls. Methods: Monocentric cross-sectional study, including 86 eyes from 43 diabetic patients with no clinical signs of diabetic retinopathy and 78 eyes from 39 controls. Patients underwent 3.0 × 3.0 mm and 4.5 × 4.5 mm swept-source optical coherence tomography angiography. Vessel density (%), foveal avascular zone area (mm2), and avascular density (%) were provided for the superficial capillary plexus and the deep capillary plexus. Results: The foveal avascular zone area at the superficial capillary plexus was larger in the study group compared to controls, irrespective of the area of the slab considered. A meaningful difference was found in the vessel density at the deep capillary plexus of the 3.0 × 3.0 mm slab (p = 0.03). Almost all the variables considered in the study showed a significant within-subject effect. Age significantly correlated with vessel density of superficial capillary plexus on 4.5 × 4.5 mm in both control and diabetic eyes. Conclusion: Diabetic patients with subclinical diabetic retinopathy feature a larger foveal avascular zone at the superficial capillary plexus compared with controls, as well as relative reduction of the vessel density at the deep capillary plexus. These findings might serve as the basis for screening between normal and diabetic subjects.

scholarly journals Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Menglin Guo ◽  
Mei Zhao ◽  
Allen M. Y. Cheong ◽  
Houjiao Dai ◽  
Andrew K. C. Lam ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optical Coherence Tomography Angiography ◽  
Automatic Segmentation ◽  
Clinical Analysis ◽  
Foveal Avascular Zone ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Hole Filling ◽  
Avascular Zone

AbstractAn accurate segmentation and quantification of the superficial foveal avascular zone (sFAZ) is important to facilitate the diagnosis and treatment of many retinal diseases, such as diabetic retinopathy and retinal vein occlusion. We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography (OCTA) images with robustness to brightness and contrast (B/C) variations. A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth (GT) was manually segmented subsequently. A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class. Subsequently, we applied largest-connected-region extraction and hole-filling to fine-tune the automatic segmentation results. A maximum mean dice similarity coefficient (DSC) of 0.976 ± 0.011 was obtained when the automatic segmentation results were compared against the GT. The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997. In all nine parameter groups with various brightness/contrast, all the DSCs of the proposed method were higher than 0.96. The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods. In conclusion, we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations. For clinical applications, this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.

Association of Foveal Avascular Zone Enlargement and Diabetic Retinopathy Progression Using Optical Coherence Tomography Angiography

2018 ◽  
Vol 2 (6) ◽  
pp. 343-350
Author(s):  
Katsuya Suzuki ◽  
Miho Nozaki ◽  
Noriaki Takase ◽  
Aki Kato ◽  
Hiroshi Morita ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Retrospective Chart Review ◽  
Foveal Avascular Zone ◽  
Diabetic Patients ◽  
Optical Coherence ◽  
Avascular Zone ◽  
Capillary Plexus

Purpose: The purpose of this article is to evaluate long-term change of the foveal avascular zone (FAZ) area in diabetic eyes using optical coherence tomography angiography (OCTA) (AngioVue, Avanti OCT, Optovue). Methods: A retrospective chart review was conducted of patients who had undergone OCTA fundus examinations with at least 12 months of follow-up. Eyes with previous laser photocoagulation and antivascular endothelial growth factor treatments were excluded. ImageJ software was used to evaluate the FAZ area in the superficial capillary plexus (SCP) and deep capillary plexus (DCP). Results: Forty eyes were analyzed in this study and divided into 3 groups: healthy controls (13 eyes), diabetic patients without diabetic retinopathy (DR) (14 eyes), and diabetic patients with DR (13 eyes). During the 22 months of follow-up, the FAZ area of eyes with DR in the DCP enlarged from 0.64 ± 0.20 mm2 to 0.70 ± 0.20 mm2 ( P = .021), which was a 10.1% increase from baseline (5.1% per year). No significant changes were observed during the study period for FAZ areas in the DCP of controls and diabetic patients without DR. Enlargement of FAZ in the DCP was significantly greater in eyes with DR progression vs those without progression (19.2% and 1.2%, respectively, P = .013). Conclusions: Our data suggest FAZ enlargement in the DCP is associated with DR progression. Assessment of the FAZ by OCTA might be useful for the evaluation of microcirculation abnormalities in DR and the onset of DR progression.

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

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.

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.

scholarly journals Quantitative Evaluation of Vascular Density in Diabetic Retinopathy Subtypes using Optical Coherence Tomography Angiography

2020 ◽  
Author(s):  
Fariba Ghassemi ◽  
Kaveh Fadakar ◽  
Sahar Berijani ◽  
Ameneh Babeli ◽  
Alireza Gholizadeh ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Normal Subjects ◽  
Vascular Density ◽  
Diabetic Patients ◽  
Multivariate Linear Regression Analysis ◽  
Optical Coherence ◽  
Potential Biomarker ◽  
Capillary Plexus

Abstract Background: To determine the discrepancy between quantitative measurement of retinal and choriocapillaris (CC) vascular density (VD) in diabetic retinopathy (DR) stages using spectral domain optical coherence tomography angiography (SD OCTA) and compare it with normal subjects.Methods: 188 eyes of 97 participants were recruited in this cross-sectional study. Macular OCTA (3x3mm) scan was performed and VD at the level of superficial capillary plexus (SCP), deep capillary plexus (DCP) and CC were measured with the device software.Results: In normal subjects, VD in SCP, DCP, and CC were higher in all subsegments. In retinal VD, all calculated parameters were reduced in the more extreme stages of DR, except for foveal VD of SCP. There was a constant pattern of decrease in VD of CC from normal cases to cases of NDR and NPDR and then a slight increase happened in the PDR stage but never touching the normal quantities. Age, fasting blood sugar, and years of diabetes mellitus were correlated with reduced VD in different segments. Multivariate linear regression analysis showed that best-corrected visual acuity (BCVA) was positively correlated with parafoveal VD at SCP and VD of foveal area at CC. VD of all subfields of macular area except foveal DCP VD showed reduced levels in diabetic macular edema (DME) patients compared to those without DME.Conclusions: The findings of the study endorse retina VD changes as a potential biomarker for DR development before retinopathy becomes clinically evident. It seems that parafoveal VD of SCP and foveal VD of CC are good biomarkers to predict VA in the diabetic patients.

scholarly journals Optical Coherence Tomography Angiography in Diabetic Patients: A Systematic Review

Biomedicines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 88
Author(s):  
Ana Boned-Murillo ◽  
Henar Albertos-Arranz ◽  
María Dolores Diaz-Barreda ◽  
Elvira Orduna-Hospital ◽  
Ana Sánchez-Cano ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Macular Oedema ◽  
Literature Search ◽  
Optical Coherence Tomography Angiography ◽  
Diabetic Macular Oedema ◽  
Oct Angiography ◽  
Diabetic Patients ◽  
Optical Coherence

Background: Diabetic retinopathy (DR) is the leading cause of legal blindness in the working population in developed countries. Optical coherence tomography (OCT) angiography (OCTA) has risen as an essential tool in the diagnosis and control of diabetic patients, with and without DR, allowing visualisation of the retinal and choroidal microvasculature, their qualitative and quantitative changes, the progression of vascular disease, quantification of ischaemic areas, and the detection of preclinical changes. The aim of this article is to analyse the current applications of OCTA and provide an updated overview of them in the evaluation of DR. Methods: A systematic literature search was performed in PubMed and Embase, including the keywords “OCTA” OR “OCT angiography” OR “optical coherence tomography angiography” AND “diabetes” OR “diabetes mellitus” OR “diabetic retinopathy” OR “diabetic maculopathy” OR “diabetic macular oedema” OR “diabetic macular ischaemia”. Of the 1456 studies initially identified, 107 studies were screened after duplication, and those articles that did not meet the selection criteria were removed. Finally, after looking for missing data, we included 135 studies in this review. Results: We present the common and distinctive findings in the analysed papers after the literature search including the diagnostic use of OCTA in diabetes mellitus (DM) patients. We describe previous findings in retinal vascularization, including microaneurysms, foveal avascular zone (FAZ) changes in both size and morphology, changes in vascular perfusion, the appearance of retinal microvascular abnormalities or new vessels, and diabetic macular oedema (DME) and the use of deep learning technology applied to this disease. Conclusion: OCTA findings enable the diagnosis and follow-up of DM patients, including those with no detectable lesions with other devices. The evaluation of retinal and choroidal plexuses using OCTA is a fundamental tool for the diagnosis and prognosis of DR.

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