Automatic Characterization of Retinal Blood Flow Using OCT Angiograms

PURPOSE: To quantitatively characterize the retinal vascular network in healthy and pathological cases using optical coherence tomography angiography (OCTA) images. METHODS: The study included 56 eyes of 28 patients as follows: 26 healthy, 20 with diabetic retinopathy (DR), 6 with age-related macular degeneration (AMD), and 4 with retinal vein occlusion (RVO). For 33 eyes (16 healthy and 17 with DR), vessel density maps were provided by the OCTA machine. An automatic algorithm classified the image (as healthy, DR, AMD, or RVO) and provided quantitative information obtained from the angiograms, including global vessel density, global fractal dimension, and fovea avascular zone (FAZ) area. Classification results were compared with the diagnosis made by a retina specialist. The quantitative values were compared with the literature and to values provided by the OCTA machine. RESULTS: The success rate of classification was 83.9%. Vessel densities obtained by our algorithm (in healthy and DR cases) were significantly lower than the values reported in previous studies using OCTA. Similarly, they were much lower than the values provided by the OCTA machine. However, vessel densities in the healthy cases were similar to or higher than (depending on the retinal layer) the recently published values that may be considered as gold standard. Our values of fractal dimension were similar to those previously reported. CONCLUSIONS: Our algorithm provides significantly improved vessel density values compared with previous studies. We believe our algorithm successfully omits false vessels. TRANSLATIONAL RELEVANCE: Accurately assessing retinal vessel density enables better evaluation of retinal disorders.