Abstract
PurposeTo compare macular and peripapillary vessel density values calculated on optical coherence tomography angiography (OCT-A) images with different algorithms, elaborate conversion formula, and compare the ability to discriminate healthy from affected eyes.MethodsCross-sectional study of healthy subjects, patients with diabetic retinopathy, and glaucoma patients (44 eyes in each group). Vessel density in the macular superficial capillary plexus (SCP), deep capillary plexus (DCP), and the peripapillary radial capillary plexus (RCP) were calculated with seven previously published algorithms. Systemic differences, diagnostic properties, reliability, and agreement of the methods were investigated.ResultsHealthy eyes exhibited higher vessel density values in all plexuses compared to diseased eyes regardless of the algorithm used (p<0.01). The estimated vessel densities were significantly different at all the plexuses (p<0.0001) as a function of method used. Inter-method reliability and agreement was mostly poor to moderate. A conversion formula was available for every method, except for the conversion between multilevel and fixed at the DCP. Substantial systemic, non-constant biases were evident between many algorithms. No algorithm outperformed the others for discrimination of patients from healthy subjects in all the retinal plexuses, but the best performing algorithm varied with the selected plexus.ConclusionsAbsolute vessel density values calculated with different algorithms are not directly interchangeable. Differences between healthy and affected eyes could be appreciated with all methods with different discriminatory abilities as a function of the plexus analyzed. Longitudinal monitoring of vessel density should be performed with the same algorithm. Studies adopting vessel density as an outcome measure should not rely on external normative databases.
Highlights
A conversion formula was available for every method, except for the conversion between multilevel and fixed at the deep capillary plexus (DCP)
Absolute vessel density values calculated with different algorithms are not directly interchangeable
Optical coherence tomography angiography (OCT-A) is a recent imaging modality that allows non-invasive, rapid, depth-resolved visualization of all the chorioretinal vascular layers. [1, 2] OCT-A devices use different algorithms, all of which are based on the assumption that erythrocytes in the blood vessels are the only moving structures within sequentially acquired B-scans and that they act as a natural motion contrast
Summary
Optical coherence tomography angiography (OCT-A) is a recent imaging modality that allows non-invasive, rapid, depth-resolved visualization of all the chorioretinal vascular layers. [1, 2] OCT-A devices use different algorithms, all of which are based on the assumption that erythrocytes in the blood vessels are the only moving structures within sequentially acquired B-scans and that they act as a natural motion contrast. [5, 7,8,9] The reported thresholding algorithms employed to binarize OCT-A angiograms and calculate vessel density, are highly heterogeneous. [4] Previous studies demonstrated good intra- and inter-operator repeatability of vessel density for images acquired in the same location, with the same angiocube size, machine, and quantification algorithm. Some instruments use their own proprietary software, these include Cirrus (AngioPlex software, Carl Zeiss Meditec, Inc., Dublin, CA, USA), [5, 10] AngioVue software (Optovue, Inc., Fremont, CA, USA), [11, 12] and RS -3000 Advance (Nidek, Gamagori, Japan). Some instruments use their own proprietary software, these include Cirrus (AngioPlex software, Carl Zeiss Meditec, Inc., Dublin, CA, USA), [5, 10] AngioVue software (Optovue, Inc., Fremont, CA, USA), [11, 12] and RS -3000 Advance (Nidek, Gamagori, Japan). [6] In the majority of the studies, images are exported and post-processed with a variety of different thresholding methods, including fixed cutoffs, [13,14,15] dynamic cutoff (e.g., mean, [9, 16, 17] ImageJ [National Institutes of Health, Bethesda, MD] default algorithm, [18] Otsu’s algorithm), [19, 20] prototype software, [21, 22] and more complex methods combining preprocessing filters and multilevel thresholds strategies. [23,24,25,26] It is still uncertain whether different algorithms lead to the same or, at least, similar results and findings from many studies could have been influenced by the algorithm utilized.
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