Repeatability of binarization thresholding methods for optical coherence tomography angiography image quantification

Variability of scan quality and perfusion density in longitudinal optical coherence tomography angiography imaging

BACKGROUND/AIMS

Optical coherence tomography angiography (OCT-A) images are subject to variability, but the extent to which learning impacts OCT-A measurements is unknown. We determined whether there is a learning effect in glaucoma patients and healthy controls imaged with OCT-A.

METHODS

Ninety-one open-angle glaucoma patients and 54 healthy controls were imaged every 4 months over a period of approximately 1 year in this longitudinal cohort study. We analysed 15°×15° scans, centred on the fovea, in one eye of each participant. Two-dimensional projection images for the superficial, intermediate and deep vascular plexuses were exported and binarised after which perfusion density was calculated. Linear mixed-effects models were used to investigate the association between perfusion density and follow-up time.

RESULTS

The mean (SD) age of glaucoma patients and healthy controls was 67.3 (8.1) years and 62.1 (9.0) years, respectively. There was a significant correlation between perfusion density and scan quality in both glaucoma patients (r=0.50 (95% CI 0.42 to 0.58); p<0.05) and healthy controls (r=0.41 (95% CI 0.29 to 0.52); p<0.05). An increase in perfusion density occurred over time and persisted, even after adjustment for scan quality (1.75% per year (95% CI 1.14 to 2.37), p<0.01).

CONCLUSIONS

Perfusion density measurements are subject to increasing experience of either the operator or participant, or a combination of both. These findings have implications for the interpretation of longitudinal measurements with OCT-A.

Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina

Purpose

This study explored the relationship among microvascular parameters as delineated by optical coherence tomography angiography (OCTA) and retinal perfusion. Here, we introduce a versatile framework to examine the interplay between the retinal vascular structure and function by generating virtual vasculatures from central retinal vessels to macular capillaries. Also, we have developed a hemodynamics model that evaluates the associations between vascular morphology and retinal perfusion.

Methods

The generation of the vasculature is based on the distribution of four clinical parameters pertaining to the dimension and blood pressure of the central retinal vessels, constructive constrained optimization, and Voronoi diagrams. Arterial and venous trees are generated in the temporal retina and connected through three layers of capillaries at different depths in the macula. The correlations between total retinal blood flow and macular flow fraction and vascular morphology are derived as Spearman rank coefficients, and uncertainty from input parameters is quantified.

Results

A virtual cohort of 200 healthy vasculatures was generated. Means and standard deviations for retinal blood flow and macular flow fraction were 20.80 ± 7.86 µL/min and 15.04% ± 5.42%, respectively. Retinal blood flow was correlated with vessel area density, vessel diameter index, fractal dimension, and vessel caliber index. The macular flow fraction was not correlated with any morphological metrics.

Conclusions

The proposed framework is able to reproduce vascular networks in the macula that are morphologically and functionally similar to real vasculature. The framework provides quantitative insights into how macular perfusion can be affected by changes in vascular morphology delineated on OCTA.

OCTA-500: A retinal dataset for optical coherence tomography angiography study

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.

Longitudinal Assessment of Macular Thickness and Microvascular Changes in Children with Sickle Cell Disease

PURPOSE

To longitudinally assess macular thickness and microvascular changes in children with sickle cell disease (SCD).

DESIGN

A retrospective consecutive series.

SUBJECTS

Children with SCD aged ≤ 18 years who had an ophthalmic examination at Boston Children's Hospital between January 1998 and August 2022.

METHODS

Qualitative and quantitative analyses of both OCT and OCT angiography (OCTA) images were performed.

MAIN OUTCOME MEASURES

Total retinal thickness measured on macular OCT, superficial capillary plexus and deep capillary plexus (DCP) vessel density (VD), and foveal avascular zone (FAZ) area measured on 6- × 6-mm OCTA scans.

RESULTS

International Classification of Diseases, 10th Revision, code search identified 303 pediatric SCD patients who underwent ophthalmic examination during the study period. OCT and OCTA images were acquired on 104 (17.2%) and 60 (9.9%) eyes at presentation and on 159 (26.2%) and 100 (16.5%) eyes at final visit, respectively. Overall, temporal retinal thinning was noted qualitatively in 35.6% of SCD patients at presentation and 39.6% at final visit. Of those patients with macular thinning, 94.6% and 90.5% had peripheral sickle cell retinopathy (SCR) at presentation and final visit. On quantitative OCT analysis, HbSS eyes had a lower retinal thickness in the fovea and temporal parafovea compared with HbSC (P < 0.05). Eyes with peripheral SCR had a larger FAZ at presentation compared with eyes without peripheral SCR (P = 0.004), a lower DCP VD at final visit in the inferior temporal macula (P = 0.03), and a higher DCP VD at final visit in the superior nasal macula (P = 0.01). Eighty eyes of 40 patients had OCT, and 34 eyes of 20 patients had both OCT and OCTA images acquired at both initial and final visits. At final visit, retinal thickness decreased at the fovea, inferior perifovea, and temporal perifovea compared with presentation (P < 0.05). In parallel, VD DCP in the superonasal quadrant increased at final visit (P = 0.03).

CONCLUSIONS

Macular retinal thinning was progressive and observed in eyes with and without peripheral SCR. Over time, there was a compensatory increase in DCP VD in the nasal macula on OCTA.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Detection of systemic cardiovascular illnesses and cardiometabolic risk factors with machine learning and optical coherence tomography angiography: a pilot study

BACKGROUND/OBJECTIVES

Optical coherence tomography angiography (OCTA) has been found to identify changes in the retinal microvasculature of people with various cardiometabolic factors. Machine learning has previously been applied within ophthalmic imaging but has not yet been applied to these risk factors. The study aims to assess the feasibility of predicting the presence or absence of cardiovascular conditions and their associated risk factors using machine learning and OCTA.

METHODS

Cross-sectional study. Demographic and co-morbidity data was collected for each participant undergoing 3 × 3 mm, 6 × 6 mm and 8 × 8 mm OCTA scanning using the Carl Zeiss CIRRUS HD-OCT model 5000. The data was then pre-processed and randomly split into training and testing datasets (75%/25% split) before being applied to two models (Convolutional Neural Network and MoblieNetV2). Once developed on the training dataset, their performance was assessed on the unseen test dataset.

RESULTS

Two hundred forty-seven participants were included. Both models performed best in predicting the presence of hyperlipidaemia in 3 × 3 mm scans with an AUC of 0.74 and 0.81, and accuracy of 0.79 for CNN and MobileNetV2 respectively. Modest performance was achieved in the identification of diabetes mellitus, hypertension and congestive heart failure in 3 × 3 mm scans (all with AUC and accuracy >0.5). There was no significant recognition for 6 × 6 and 8 × 8 mm for any cardiometabolic risk factor.

CONCLUSION

This study demonstrates the strength of ML to identify the presence cardiometabolic factors, in particular hyperlipidaemia, in high-resolution 3 × 3 mm OCTA scans. Early detection of risk factors prior to a clinically significant event, will assist in preventing adverse outcomes for people.

Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review

Background

This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images.

Methods

A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evaluated each of the selected articles in order to assess the implementation of ImageJ in OCT-A images.

Results

ImageJ can aid in reducing artifacts, enhancing image quality to increase the accuracy of the process and analysis, processing and analyzing images, generating comparable parameters such as the parameters that assess perfusion of the layers (vessel density (VD), skeletonized density (SD), and vessel length density (VLD)) and the parameters that evaluate the structure of the layers (fractal dimension (FD), vessel density index (VDI), and lacunarity (LAC)), and the foveal avascular zone (FAZ) that are used widely in the retinal and choroidal studies), and establishing diagnostic criteria. It can help to save time when the dataset is huge with numerous plugins and options for image processing and analysis with reliable results. Diverse studies implemented distinct binarization and thresholding techniques, resulting in disparate outcomes and incomparable parameters. Uniformity in methodology is required to acquire comparable data from studies employing diverse processing and analysis techniques that yield varied outcomes.

Conclusion

Researchers and professionals might benefit from using ImageJ because of how quickly and correctly it processes and analyzes images. It is highly adaptable and potent software, allowing users to evaluate images in a variety of ways. There exists a diverse range of methodologies for analyzing OCTA images through the utilization of ImageJ. However, it is imperative to establish a standardized strategy to ensure the reliability and consistency of the method for research purposes.

Image processing and quantification analysis for optical coherence tomography angiography in epiretinal membrane

BACKGROUND

To explore image processing methods for optical coherence tomography angiography (OCTA) of the epiretinal membrane (ERM), and to evaluate the impact of ERM on vision by analyzing the retinal vasculature.

METHODS

Thirty eyes of 30 patients with idiopathic ERM who underwent OCTA were retrospectively evaluated. Image processing of OCTA, including the Mexican hat filter (MHF) and exclusion of the foveal avascular zone (FAZ), was attempted using Fiji. OCTA parameters, including vessel density (VD), fractal dimension (FD), and vessel tortuosity (VT), were measured for large vessels only, capillaries only, and the whole vasculature. Pearson correlation analysis was used to evaluate the correlations between best-corrected visual acuity (BCVA) and OCTA parameters.

RESULTS

The correlations between BCVA and retinal vasculature were much increased when the capillaries only instead of the whole vasculature was used for analysis. Both higher VD and FD of capillaries were correlated with better BCVA, and MHF largely increased their correlations (P < 0.0001 for both). In contrast, both higher VD and FD of the large vessels were associated with poorer BCVA (P = 0.042 and 0.049, respectively). A higher VT of capillaries was correlated with better BCVA, and both MHF and exclusion of the FAZ were necessary to reveal their correlations (P = 0.028) CONCLUSIONS: Separation of large vessels and capillaries was necessary to reveal the correlation between retinal vasculature and BCVA in ERM. MHF was necessary to elucidate all microvascular parameters of capillaries, and exclusion of the FAZ was mandatory for evaluation of VT.

Pilot study of optical coherence tomography angiography-derived microvascular metrics in hands and feet of healthy and diabetic people

Optical coherence tomography angiography (OCTA) is a non-invasive, high-resolution imaging modality with growing application in dermatology and microvascular assessment. Accepted reference values for OCTA-derived microvascular parameters in skin do not yet exist but need to be established to drive OCTA into the clinic. In this pilot study, we assess a range of OCTA microvascular metrics at rest and after post-occlusive reactive hyperaemia (PORH) in the hands and feet of 52 healthy people and 11 people with well-controlled type 2 diabetes mellitus (T2DM). We calculate each metric, measure test-retest repeatability, and evaluate correlation with demographic risk factors. Our study delivers extremity-specific, age-dependent reference values and coefficients of repeatability of nine microvascular metrics at baseline and at the maximum of PORH. Significant differences are not seen for age-dependent microvascular metrics in hand, but they are present for several metrics in the foot. Significant differences are observed between hand and foot, both at baseline and maximum PORH, for most of the microvascular metrics with generally higher values in the hand. Despite a large variability over a range of individuals, as is expected based on heterogeneous ageing phenotypes of the population, the test-retest repeatability is 3.5% to 18% of the mean value for all metrics, which highlights the opportunities for OCTA-based studies in larger cohorts, for longitudinal monitoring, and for assessing the efficacy of interventions. Additionally, branchpoint density in the hand and foot and changes in vessel diameter in response to PORH stood out as good discriminators between healthy and T2DM groups, which indicates their potential value as biomarkers. This study, building on our previous work, represents a further step towards standardised OCTA in clinical practice and research.

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives

When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.

The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage

The growing health and economic burden of bone fractures, their intricate multiscale features and the existing knowledge gaps in the comprehension of micro-scale bone damage occurrence make fracture diagnosis a challenging issue. In this scenario, deep-learning and artificial intelligence embody the new frontier of healthcare system, by overcoming the subjectivity of clinicians in the analysis of medical images. However, the preliminary attempts in exploiting the power of machine learning algorithms such as neural networks are still limited to bone macro-scale, while there is an evident lack in their application to smaller scales, where damage starts nucleating. Currently, speculations at the micro-scale are only feasible with the aid of high-resolution imaging techniques, that are particularly time consuming in terms of output images analysis. In this context, this works aims at combining the visualization of the micro-crack propagation mechanism with the promising application of convolutional neural networks. The implemented artificial intelligence tool is based for the first time on a large number of human synchrotron images coming from healthy and osteoporotic femoral heads tested under micro-compression. The designed convolutional neural networks are able to automatically detect lacunae and micro-cracks at different compression levels with high accuracy levels; indeed, with the baseline setup, networks achieve more than 0.99 level of accuracy for both cracks and lacunae, and more than 0.87 of the meanIoU adopted as validation metric. This approach is particularly encouraging for the development of powerful recognition system to comprehend bone micro-damage initiation and propagation, paving the way to the application of machine learning studies to bone micromechanics. This could be additionally crucial for future patient specific micro-scale observations to be related to the clinical practice.

The Impact of Image Processing Algorithms on Optical Coherence Tomography Angiography Metrics and Study Conclusions in Diabetic Retinopathy

Purpose

The purpose of this study was to evaluate the impact of image processing on quantitative metrics in optical coherence tomography angiography (OCTA) images and study conclusions in patients with diabetes.

Methods

This was a single center, retrospective cross-sectional study. OCTA imaging with the Cirrus HD-OCT 5000 AngioPlex of patients with diabetes was performed. The 8 × 8 mm superficial slab images underwent 4 different preprocessing methods (none, background subtraction [BGS], foveal avascular zone brightness adjustment, and contrast limited adaptive histogram equalization [CLAHE]) followed by 4 different binarization algorithms (global Huang, global Otsu, local Niblack, and local Phansalkar) in ImageJ. Vessel density (VD), skeletonized VD (SVD), and fractal dimension (FD) were calculated. Mixed-effect multivariate linear regressions were performed.

Results

Two hundred eleven scans from 104 patients were included. Of these scans, 67 (31.8%) had no diabetic retinopathy (DR), 99 (46.9%) had nonproliferative DR (NPDR), and 45 (21.3%) had proliferative DR (PDR). Forty-eight of 211 (22.7%) scans had diabetic macular edema (DME). The image processing method used significantly impacted values of VD, SVD, and FD (all P -values < 0.001). On multivariate analysis, the image processing method changed the clinical variables significantly associated with VD, SVD, and FD. However, BGS and CLAHE yielded more consistent significant covariates across multiple binarization algorithms.

Conclusions

The image processing method can impact the conclusions of any given study analyzing quantitative OCTA metrics. Thus, caution is urged in the interpretation of such studies. Background subtraction or CLAHE may play a role in the standardization of image processing.

Translational Relevance

This work proposes strategies to achieve robust and consistent analysis of OCTA imaging, which is especially important for clinical trials.

Classification-based framework for binarization on mice eye image in vivo with optical coherence tomography

Optical coherence tomography (OCT) angiography has drawn much attention in the medical imaging field. Binarization plays an important role in quantitative analysis of eye with optical coherence tomography. To address the problem of few training samples and contrast-limited scene, we proposed a new binarization framework with specific-patch SVM (SPSVM) for low-intensity OCT image, which is open and classification-based framework. This new framework contains two phases: training model and binarization threshold. In the training phase, firstly, the patches of target and background from few training samples are extracted as the ROI and the background, respectively. Then, PCA is conducted on all patches to reduce the dimension and learn the eigenvector subspace. Finally, the classification model is trained from the features of patches to get the target value of different patches. In the testing phase, the learned eigenvector subspace is conducted on the pixels of each patch. The binarization threshold of patch is obtained with the learned SVM model. We acquire a new OCT mice eye (OCT-ME) database, which is publicly available at https://mip2019.github.io/spsvm. Extensive experiments were performed to demonstrate the effectiveness of the proposed SPSVM framework.

Segmentation methods and morphometry of confocal microscopy imaged corneal epithelial cells

PURPOSE

To develop and explore automated cell identification and segmentation methods for morphometry of confocal microscopy imaged corneal epithelial cells using ImageJ software.

METHODS

In vivo confocal microscopy images of the intermediate (wing) and basal cell layers of the central and peripheral corneas of 20 healthy participants were analysed. The intermediate and basal cell areas obtained using the two new techniques (i.e., manual- and auto- thresholding) were compared with the widely used manual tracing technique. A predefined range of epithelial cell morphometric parameters was used as image descriptors to improve cell identification and segmentation.

RESULTS

The mean intermediate cell area obtained using the manual tracing (central; 120 ± 14 µm², peripheral; 123 ± 15 µm²) was statistically similar (p > 0.05) to the manual thresholding (central; 119 ± 7 µm², peripheral; 119 ± 8) but not with the auto thresholding technique (central; 101 ± 8 µm², peripheral; 101 ± 7 µm²). Bland-Altman limits of agreement for the mean difference (measurement bias) in central and peripheral intermediate cell area determined via manual tracing and manual thresholding techniques were 1 µm² (+25 to - 23 µm²) and 4 µm² (+29.8 to - 21.9 µm²). There were statistically significant differences in basal cell area between the three methods.

CONCLUSION

The manual thresholding technique may be used for automated identification and segmentation of corneal epithelial intermediate cells (central and peripheral) for assessing various morphometric parameters. However, measurement of the corneal epithelial basal cells is less reliable using thresholding techniques.

Corneal morphology correlates with choriocapillaris perfusion in myopic children

AIMS

The present study investigated the difference in choriocapillaris (CC) perfusion between different AL/K ratio groups with similar spherical equivalent refraction (SER) and analyzed factors affecting CC perfusion.

METHODS

This cross-sectional study included 129 children with low-to-moderate myopia. Axial length (AL), average K-reading (Ave-K), and SER were measured. Choroidal vascularity, including the total choroidal area (TA), choroidal luminal area (LA), stromal area (SA), choroidal vascularity index (CVI), CC flow voids (FVs), and FVs%, was obtained using optical coherence tomography angiography.

RESULTS

Participants with similar SER were divided into two groups (high AL/K ratio, n = 57; low AL/K ratio, n = 72). The high AL/K group had lower LA, TA, and CVI (P < 0.01) and lower FVs (inner ring and fovea, P < 0.05) and FVs% (outer ring, inner ring, and fovea, P < 0.05). The AL/K ratio and FVs% were negatively correlated in the outer ring (r =  - 0.174, P < 0.05) and inner ring (r =  - 0.174, P < 0.05). The Ave-K and inner FVs (r = 0.178, P < 0.05), outer FVs% (r = 0.175, P < 0.05), and inner FVs% (r = 0.196, P < 0.05) were positively correlated. In stepwise multiple regression for the outer ring, the horizontal CVI was related to FVs (β = 0.175, P < 0.05), and the vertical CVI was related to FVs% (β = 0.232, P < 0.01). Independent risk factors associated with inner FVs area were vertical CVI (β = 0.329; P < 0.001) and SER (β =  - 0.196, P < 0.05); FVs% was also associated with vertical CVI (β = 0.360, P < 0.01) and SER (β =  - 0.196, P < 0.05).

CONCLUSION

With a similar SER, myopic eyes with a higher AL/K ratio maintained more CC perfusion and lower CVI, which may indicate rapid myopic progression. Low K-reading eyes had more CC perfusion and less CVI, which may explain the relatively poor myopia control efficacy in the clinic.

Retinal Vessel Density in Age-Related Macular Degeneration Patients with Geographic Atrophy

We compared the retinal vessel density and inner retinal thickness in patients who had one eye with geographic atrophy (GA) and a fellow eye with intermediate age-related macular degeneration (iAMD). The vessel density from the superficial vascular complex (SVC) and deep vascular complex (DVC) through optical coherence tomography angiography and the thickness of the nerve fiber layer, ganglion cell-inner plexiform layer (GCIPL), inner nuclear layer (INL), outer nuclear layer (ONL) on a structural optical coherence tomography thickness map were measured in 28 eyes of 14 GA patients with iAMD in the fellow eye. GA eyes had significantly lower vessel density in the SVC (26.2 ± 3.9% vs. 28.3 ± 4.4%; p = 0.015) and DVC (24.2 ± 2.6% vs. 26.8 ± 1.9%; p = 0.003) than fellow eyes (iAMD). GCIPL and ONL were significantly thinner in GA eyes than in the fellow eyes (p = 0.032 and 0.024 in the foveal areas, p = 0.029 and 0.065 in the parafovea areas, respectively). Twenty-four eyes of 12 patients were followed up for 2 years and seven of the fellow eyes (58.3%) developed GA during the follow-up period and showed reduced vessel density in the SVC (26.4 ± 3.0% vs. 23.8 ± 2.9%; p = 0.087) and DVC (25.8 ± 2.2% vs. 22.4 ± 4.4%; p = 0.047) compared to baseline. Vessel density and GCIPL thickness map measurements are potential GA markers in non-neovascular AMD.

MULTISCALE CORRELATION OF MICROVASCULAR CHANGES ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY WITH RETINAL SENSITIVITY IN DIABETIC RETINOPATHY

PURPOSE

To assess global, zonal, and local correlations between vessel density changes measured by optical coherence tomography angiography and retinal sensitivity measured by microperimetry across diabetic retinopathy severity.

METHODS

Diabetic patients and nondiabetic controls underwent optical coherence tomography angiography imaging and microperimetry testing. Pearson's correlation was used to assess associations between average sensitivity and skeletonized vessel density (SVD) or foveal avascular zone area centrally. Linear mixed effects modeling was used to assess relationships between local SVD measurements and their spatially corresponding retinal sensitivity measurements.

RESULTS

Thirty-nine eyes from 39 participants were imaged. In all slabs, there was a statistically significant positive correlation between retinal sensitivities and SVDs on both global and zonal scales. No statistically significant correlation was found between central retinal sensitivities and the foveal avascular zone areas. Assessment of 1,136 spatially paired retinal sensitivity and SVD measurements revealed a statistically significant local relationship; this seemed to be driven by eyes with proliferative diabetic retinopathy that had reduced retinal sensitivities.

CONCLUSION

This study supports positive correlations between SVD and retinal sensitivity at global and zonal spatial scales in diabetic eyes. However, our analysis did not find evidence of statistically significant correlations between retinal sensitivity and SVD on a local scale until advanced diabetic retinopathy.

Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method's performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

The impact of different thresholds on optical coherence tomography angiography images binarization and quantitative metrics

Optical coherence tomography angiography (OCTA) provides several data regarding the status of retinal capillaries. This information can be further enlarged by employing quantitative metrics, such as vessel density (VD). A mandatory preliminary step of OCTA quantification is image binarization, a procedure used to highlight retinal capillaries on empty background. Although several binarization thresholds exist, no consensus is reached about the thresholding technique to be used. In this study, we tested thirteen binarization thresholds on a dataset made by thirty volunteers. The aim was to assess the impact of binarization techniques on: (I) detection of retinal capillaries, assessed by the calculation of overlapping percentages between binarized and original images; (II) quantitative OCTA metrics, including VD, vessel tortuosity (VT) and vessel dispersion (Vdisp); (III) foveal avascular zone (FAZ) detection. Our findings showed Huang, Li, Mean and Percentile as highly reliable binarization thresholds (p < 0.05), whereas the worst binarization thresholds were Intermodes, MaxEntropy, RenylEntropy and Yen (p < 0.05). All the thresholds variably underestimated VD metric and FAZ detection, with respect to the original OCTA images, whereas VT and Vdisp turned out to be more stable. The usage of a Fixed threshold resulted extremely useful to reduce VD and FAZ underestimations, although bound to operators' experience.

Macular Vessel Density in Diabetic Retinopathy Patients: How Can We Accurately Measure and What Can It Tell Us?

Diabetic retinopathy is one of the leading causes of blindness worldwide. Optical coherence tomography angiography (OCTA) is a non-invasive technology that provides depth-resolved images of the chorioretinal vasculature and allows for the understanding of the changes in vasculature with diabetic retinopathy. Not only can it provide qualitative information, but OCTA can also provide quantitative information about the vasculature in patients with diabetic retinopathy. Macular vessel density is one of the quantitative metrics that can be obtained from OCTA images. This is a repeatable and non-subjective measurement that can provide valuable insight into the pathophysiology of diabetic retinopathy. In this non-systematic review, the measurement of macular vessel density in diabetic retinopathy and the reasons for its importance in the diagnosis and management of patients with diabetes and varying severities of diabetic retinopathy is discussed.

Effect of vessel enhancement filters on the repeatability of measurements obtained from widefield swept-source optical coherence tomography angiography

We assessed the inter-visit repeatability of 15 × 9-mm² swept-source OCTA (SS-OCTA; PLEX Elite 9000, Carl Zeiss Meditec) metrics in 14 healthy participants. We analysed the perfusion density (PD) of large vessels, superficial capillary plexus (SCP), and deep capillary plexus (DCP) as well as choriocapillaris flow voids in 2 different regions: the macular region and peripheral region. Also, retinal plexus metrics were processed further using different filters (Hessian, Gabor and Bayesian) while choriocapillaris flow voids were calculated with 1 and 1.25 standard deviation (SD) thresholding algorithms. We found excellent repeatability in the perfusion densities of large vessels (ICC > 0.96). Perfusion densities varied with different filters in the macular region (SCP: 24.12–38.57% and DCP: 25.16–38.50%) and peripheral (SCP: 30.52–39.84% and DCP: 34.19–41.60%) regions. The ICCs were lower in the macular region compared to the peripheral region and lower for DCP than for SCP. For choriocapillaris flow voids, the 1.25 SD threshold resulted in fewer flow voids, while a good ICC (ICC > 0.81) was achieved using either threshold settings for flow void features in both regions. Our results suggest good repeatability of widefield SS-OCTA for the measurements of retinal perfusion density and choriocapillaris flow voids, but measurements from different filters should not be interchanged.

Repeatability and reproducibility of vessel density measurements on optical coherence tomography angiography in diabetic retinopathy

PURPOSE

Understanding the precision of measurements on and across optical coherence tomography angiography (OCTA) devices is critical for tracking meaningful change in disease. The purpose of this study is to investigate the repeatability and reproducibility of vessel area density and vessel skeleton density measurements from various commercial OCTA devices in diabetic eyes.

METHODS

Patients were imaged three consecutive times each on three different OCTA devices. En face OCTA images of the superficial capillary plexus, deep capillary plexus, and full retinal layer were exported for analysis. Vessel area density and vessel skeleton density were calculated. The coefficient of repeatability (CoR) was calculated to assess the repeatability of these measurements, and linear mixed models were utilized to assess the reproducibility of these measurements.

RESULTS

Forty-four eyes from 27 diabetic patients were imaged. Normalized CoR values ranged between 3.44 and 6.65% when calculated for vessel area density and between 1.35 and 23.39% when calculated for vessel skeleton density. When stratified by disease severity, the swept-source OCTA device consistently produced the smallest CoR values for vessel area density in the full retinal layer. Vessel area density measurements were repeatable across the two spectral-domain devices in the full retinal layer when all severities were combined, as well as in diabetic patients without retinopathy, mild nonproliferative diabetic retinopathy (NPDR), and moderate NPDR.

CONCLUSION

Vessel area density measured in the full retinal layer may be a more precise measure than vessel skeleton density to follow diabetic retinopathy patients both on the same device and across devices.