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

Abstract

PurposeThe 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.MethodsThis 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.ResultsTwo 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.ConclusionsThe 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 RelevanceThis work proposes strategies to achieve robust and consistent analysis of OCTA imaging, which is especially important for clinical trials.