Choriocapillaris (CC) plays an important role in multiple disease pathologies such as age-related macular degeneration (AMD), diabetic retinopathy (DR), uveitis, and glaucoma (1-7). Traditional dye-based angiography such as indocyanine green angiography has been used to image CC in vivo (8), but the limited resolution of dye based-angiography makes it impossible to achieve the level of CC visualization necessary for accurate quantification. Better visualization of CC in vivo and reliable quantification of the CC is needed to improve our understanding of the CC involvement in multiple eye diseases and help facilitate early diagnosis, improve our understanding of disease progression, and monitor treatment.
Recently, optical coherence tomography (OCT) and OCT based angiography (OCTA) (9,10) have been introduced into clinical ophthalmology (11-22). OCTA is a non-invasive, safe, fast, and cost-effective technology that extracts the intrinsic signals due to moving particles, such as red blood cells, in functional vessels to achieve an in vivo angiogram. Due to its fast imaging speed (seconds), and high spatial resolution (~15–20 µm laterally, ~6 µm axially), a great deal of effort has been devoted to achieving accurate visualization and quantification of CC with OCTA (23-29). In particular, our group has reported two different methods to segment and quantify flow deficits (FDs) in en face CC images with swept-source OCTA (SS-OCTA). The first method (29) utilized a threshold based on the standard deviation (SD) calculated from CC images of 20 eyes randomly chosen from a group of 20–30 years old. This SD method has been used to quantify FDs in the CC in a normal database. One of its potential limitations is that it requires a collection of normal database as the reference. Therefore, it would not be able to directly translate between different scanning protocols, nor from one manufacturer’s OCTA system to another. To mitigate this limitation, we have developed an alternative method (30) that utilized a fuzzy C-means (FCM) algorithm to segment and quantify CC. This FCM method was initially developed by Dunn in the early seventies (31) to cluster large datasets into different categories, and now it is widely used in bioinformatics and machine learning. The FCM was used in OCTA to assign each pixel into different memberships (CC flows or CC FDs), based on the histogram of the whole image (30). The advantage of the FCM method is that it can be applied across different OCTA systems or different scanning protocols.
In this study, we compared these two CC quantification methods, the SD method and the FCM method, using a normal database of 164 subjects to determine if the two methods produce comparable quantitative CC parameters. In addition to providing guidance on the use of these algorithms, a good agreement between the two methods would further validate the underlying approach to CC FDs quantification.
In this study, we used an existing SS-OCTA database available from the University of Miami and the University of Washington. The database was obtained from a cross-sectional, study of normal eyes over a wide range of ages that was approved by the Institutional Review Board of Medical Sciences Subcommittee at University of Miami, Miller School of Medicine. The study at the University of Washington was also approved by the Institutional Review Board of Medical Sciences Subcommittee at University of Washington, Seattle. The tenets of the Declaration of Helsinki and the Health Insurance Portability and Accountability Act of 1996 regulations were followed. Informed consents were obtained from all subjects before participation. Subjects with a normal ocular history, no visual complains, and no identified optic disc, retinal, or choroidal pathology on examination were enrolled in the study between November 2016 to February 2018. 15 to 25 subjects were included in each decade ranging from 20s to 80s.
For all subjects, both eyes were scanned using the PLEXTM Elite 9000 (Carl Zeiss Meditec, Dublin, CA), and both 3 mm × 3 mm and 6 mm × 6 mm SS-OCTA images centered on the fovea were acquired. The right eyes were selected for analysis by default unless poor signal strength (<7) or severe motion artifacts were present. Due to the variability of the OCT signal strength among individual subjects, all the images were normalized to the signal strength of nine before OCTA maps were obtained for further CC analysis (32). A semi-automatic segmentation software (33) was then applied to both the OCT and OCTA images to produce a 20 µm thickness CC slab located beneath Bruch’s membrane. En face CC flow images were compensated using the corresponding en face structural images for any possible signal loss due to overlying anatomy (29). Subsequently, retinal projection artifacts were removed (32) before the CC OCTA images were further processed for quantification.
Two previously published methods, the SD and FCM methods, were applied to segment FDs (Figure 1). Briefly, in the SD method, the mean and SD from the reference normal database were used to determine a global threshold. Pixels with an intensity lower than one SD below its mean were segmented as FDs. In the FCM method, all pixels were clustered into memberships, CC vasculature and FDs, based on their intensity and the histogram distribution of the whole image. The number of memberships were automatically determined using the elbow method (34) and could vary from image to image. The first membership corresponds to the FDs, as it is the membership with lowest OCTA signal intensities.
Both methods generated CC FD binary maps for all subjects. A final step before the comparison was to remove FDs with an equivalent diameter smaller than 24 µm from the CC FD maps, since these are smaller than the estimated inter-capillary distance (ICD) and are likely to represent noise (30,35). Subsequently, FD density (FDD) and mean FD size (MFDS) (30) were calculated based on the respective CC FD binary maps (Figure 1D,E,F,G,H,I,M,N,O,P,Q,R). In each 3 mm × 3 mm scan, we quantified three regions centered at the fovea: a 1mm circle (C1), a 1.5 mm rim (R1.5) and a 2.5 mm circle (C2.5). Similarly, in each 6 mm × 6 mm scans, we quantified five regions centered at the fovea: C1, R1.5, C2.5, a 2.5 mm rim (R2.5) and a 5 mm circle (C5). Positions of all quantified circles and rims are illustrated in Figure 2. Fovea positions were identified using a method previously described (36).
Statistical analyses were performed using MATLAB (R2016b; MathWorks, Inc., Natick, Massachusetts, USA) and Prism (GRAPHPAD software, San Diego, CA, USA). The nonparametric Spearman correlations and Bland-Altman plots were used to describe the agreement between the methods (37). Repeatability was quantified as the coefficient of variation (CV) (38).
We first used a published dataset of 10 normal subjects and 11 subjects with drusen secondary to AMD (29) to evaluate the intra-visit repeatability of both the SD and FCM methods (Table 1). Repeatability of the SD method and FCM methods was comparable for FDD measurements while the FCM method provided better repeatability for MFDS measurements. Both methods provided better repeatability in the normal subjects group compared with repeatability in the drusen subjects group.
We then used both the SD and FCM methods on a total of 164 eyes from 164 subjects (age: 56±19, 59% females) from the normal database. Both 3 mm × 3 mm and 6 mm × 6 mm OCTA datasets were analyzed using both methods for all 164 normal eyes. For the 3 mm × 3 mm scans, FDD and MFDS were measured over three regions (C1, R1.5, C2.5), while for the 6 mm × 6 mm scans, five regions (C1, R1.5, R2.5, C2.5, C5) were used.
To compare quantitative CC measurements of FDD and MFDS produced by two methods, we applied Spearman’s correlation analysis and Bland-Altman agreement analysis. Figure 3 demonstrates these analyses of the FDD in the 3 mm × 3 mm scans. The correlation between two methods was the strongest in C1 (r=0.94, P<0.0001), followed by C2.5 (r=0.84, P<0.0001) and then R1.5 (r=0.83, P<0.0001). Overall, the FCM method produced lower FDD compared with the SD method. We have observed a mean bias of −0.0403 between the SD method and the FCM method in C1, with a limit of agreement (LOA) of −0.0991 to 0.0186. C2 has a mean bias of −0.0376 and a LOA of −0.0878 to 0.0126. In R1.5, the mean bias is −0.0370 while the LOA is −0.0868 to 0.0127. Similar trend was also found in MFDS in the 3 mm × 3 mm scans (Figure 4). The correlation between the two methods was the strongest in C1 (r=0.93, P<0.0001), followed by C2.5 (r=0.86, P<0.0001) and then R1.5 (r=0.83, P<0.0001). Overall, the FCM method produced larger MFDS compared with the SD method. There was a mean bias of 327.9 µm2 (9.7 pixels) between the SD method and the FCM method in C1 [LOA: −752.2µm2 (21.9 pixels) to 1,478 µm2 (43.1 pixels)], a mean bias of 409 µm2 (11.9 pixels) in C2.5 [LOA: −303.5 µm2 (8.8 pixels) to 1,121 µm2 (32.6 pixels)] and a mean bias of 284.6 µm2 (8.3 pixels) in R1.5 [LOA: 341.9 µm2 (9.9 pixels) to 911.1 µm2 (26.5 pixels)]. Our data indicate that in the 3 mm × 3 mm scans, the SD method resulted in smaller FDs compared to the FCM method, since the FCM method gave lower FDD yet larger MFDS on average. This suggests that the SD method could be more sensitive to smaller FDs while FCM method is more specific to larger FDs in 3 mm × 3 mm scans.
In the 6 mm × 6 mm scans, similar trends were observed for the correlations between the SD method and FCM method (Figures 5,6). Correlation was stronger in the central macular regions and weaker in parafoveal and perifoveal regions. For FDD in circles (Figure 5), the correlation between two methods was the strongest in the central C1 (r=0.94, P<0.0001), followed by C2.5 (r=0.86, P<0.0001) and then C5 (r=0.78, P<0.0001). Similarly, in the rims, the R1.5 (r=0.85, P<0.0001) showed a stronger correlation than R2.5 (r=0.81, P<0.0001). On average, in all five regions, the FCM method resulted in higher FDD compared with the SD method. The mean bias in the C1 is 0.0188 (LOA: −0.0376 to 0.0752), 0.0212 for C2.5 (LOA: −0.0331 to 0.0757) and 0.0198 for C5 (LOA: −0.0226 to 0.0623). In the rims, the mean bias in R1.5 is 0.0218 (LOA: −0.0333 to 0.0769) and 0.0193 for R2.5 (LOA: −0.0196 to 0.0583). Similar trends for the correlations were found in MFDS as well (Figure 6). In circles, the correlation was the strongest in C1 (r=0.93, P<0.0001), followed by C2.5 (r=0.90, P<0.0001) then C5 (r=0.86, P<0.0001). In rims, the correlation was stronger in R1.5 (r=0.88, P<0.0001) than R2.5 (r=0.85, P<0.0001). Overall, MFDS resulted in stronger correlations in all regions but C1 when compared with FDD. In terms of differences, the mean bias in C1 is 73.6 µm2 (0.5 pixels) with a LOA of −1,118 µm2 (8.1 pixels) to 1,265 µm2 (9.2 pixels), for C2.5, the mean bias is 36.5 µm2 (0.3 pixels) and the LOA is −669 µm2 (4.9 pixels) to 597 µm2 (4.3 pixels), for C5, the mean bias is −116.1µm2 (0.8 pixels) and the LOA is −669 µm2 (4.9 pixels) to 597 µm2 (4.3 pixels). In rims, the mean bias in R1.5 is −116.1 µm2 (0.8 pixels) and the LOA is −669 µm2 (4.9 pixels) to 597 µm2 (4.3 pixels), for R2.5, the mean bias is −220.2 µm2 (1.6 pixels) and the LOA is −615.5 µm2 (4.5 pixels) to 175.1 µm2 (1.3 pixels).
In this study, we have compared two methods for the quantification of CC FDs and found that both the SD and FCM methods provided good repeatability in normal subjects and drusen subjects, with the FCM method resulting in comparable or lower CV. We also demonstrated strong correlations between both methods for the quantitative CC parameters (r: 0.78–0.94, all P<0.0001) derived from various regions of both the 3 mm × 3 mm scans and 6 mm × 6 mm scans. Overall, the MFDS measurements resulted in comparable or better correlations compared to FDD measurements, especially in the 6 mm × 6 mm scans.
Visualizing and quantifying CC have been a keen interest of many investigators (24,25,29,37,39-45), especially since the recent technological advances of commercial OCTA systems. However, researchers should be cautious and vigilant while conducting quantitative analyses of CC using OCTA. There are a number of factors that could potentially compromise the integrity of CC quantification data such as scan signal intensity and strength, CC slab segmentation, CC vasculature (or FDs) segmentation algorithm, and appropriate quantitative parameters (such as FDD and MFDS in this study). In our study, all included scans had a signal strength index higher than 7 and manual segmentation was employed to ensure correct CC slab. We have also employed a compensation strategy that uses structural OCT signal to correct for attenuated OCTA signals in the CC. Both methods compared in this report showed satisfactory repeatability (FCM method all CV ≤5.04%, SD method all CV ≤7.43%), strong correlations (all P<0.0001, all r>0.83 for 3 mm × 3 mm and all r>0.78 for 6 mm × 6 mm), and good agreement.
In summary, we have demonstrated strong correlations of quantitative CC metrics between the SD and FCM methods. Overall, the correlation was stronger in the central macular regions than in the parafoveal and perifoveal regions. For the same regions (C1, R1.5, C2.5), 6 mm × 6 mm scans resulted in similar or stronger correlations compared to 3 mm × 3 mm scans. For either 3 mm × 3 mm or 6 mm × 6 mm scan pattern, the MFDS measurements resulted in comparable or stronger correlations compared with the FDD measurements. In terms of agreement, 6 mm × 6 mm scans resulted in smaller mean biases compared with the same regions (C1, R1.5, C2.5) in the 3 mm × 3 mm scans.
There are several limitations in the current study. Firstly, we lack a ground truth of accepted in vivo CC vasculature and FDs measurements for comparison with our quantitative analyses. However, currently there are no clinically available imaging techniques that can provide such a ground truth. It remains possible that future technological developments of adaptive optics OCTA or faster SS-OCTA instruments could solve this problem. Secondly, we only have compared the two methods on a normal population, and we did not include diseased population in the comparison part of this study. Future studies are needed to investigate the correlation and agreement between the SD method and the FCM method in diseased eyes. Regardless of these limitations, strong correlations and satisfactory agreements of quantitative CC parameters using the SD and FCM methods were observed. Both methods could be used for future analyses depending on specific study designs, such as availability of a normal database.
Funding: Research supported by grants from the National Eye Institute (R01EY024158, R01EY028753), the Salah Foundation, an unrestricted grant from the Research to Prevent Blindness, Inc., New York, NY, and the National Eye Institute Center Core Grant (P30EY014801) to the Department of Ophthalmology, University of Miami Miller School of Medicine. The funding organization had no role in the design or conduct of this research.
Conflicts of Interest: Dr. Gregori, Dr. Wang and Dr. Rosenfeld received research support from Carl Zeiss Meditec, Inc. Dr. Gregori and the University of Miami co-own a patent that is licensed to Carl Zeiss Meditec, Inc. Dr. Rosenfeld is a consultant for Achillion Pharmaceuticals, Acucela, Boehringer-Ingelheim, Carl Zeiss Meditec, Chengdu Kanghong Biotech, Ocunexus Therapeutics, Healios K.K, Hemera Biosciences, F. Hoffmann-La Roche Ltd., Isarna Pharmaceuticals, Lin Bioscience, NGM Biopharmaceuticals, and Unity Biotechnology. Dr. Rosenfeld has equity interest in Apellis, Digisight, and Ocudyne. Dr. Wang discloses intellectual property owned by the Oregon Health and Science University and the University of Washington. Dr. Wang also receives research support from Tasso Inc., Moptim Inc., and Colgate Palmolive Company. He is a consultant to Insight Photonic Solutions, Kowa, and Carl Zeiss Meditec. The remaining authors have no conflicts of interest to declare.
Ethical Statement: The study was approved by the Institutional Review Board of Medical Sciences Subcommittee at University of Miami, Miller School of Medicine. The study at the University of Washington was also approved by the Institutional Review Board of Medical Sciences Subcommittee at University of Washington, Seattle. The tenets of the Declaration of Helsinki and the Health Insurance Portability and Accountability Act of 1996 regulations were followed. Informed consents were obtained from all subjects before participation.
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