Background: Decrease of ocular blood flow has been linked to the pathogenesis of ocular diseases such as glaucoma and age-related macular degeneration. Current methods that measure the pulsatile blood flow have major limitations, including the assumption that ocular rigidity is the same in all eyes. Our group has recently developed a new method to measure the pulsatile choroidal volume change by direct visualization of the choroid with OCT imaging and automated segmentation. Our goal in this study is to describe the distribution of PCBF in a healthy Caucasian population.
Methods: Fifty-one subjects were recruited from the Maisonneuve-Rosemont Hospital Ophthalmology Clinic and underwent PCBF measurement in one eye. The distribution of PCBF in healthy eyes was assessed.
Results: The distribution of PCBF among the healthy eyes was found to be 3.94±1.70 μL with this technique.
Conclusions: This study demonstrates the normal range of PCBF values obtained in a healthy Caucasian population. This technique could be used for further investigation of choroid pulsatility and to study glaucoma pathophysiology.
Background: The oxygen induced retinopathy rodent model is widely used, notably for the assessment of developmental dystrophies in preclinical studies of vascular retinal diseases. Typically, the quantification of vessel tufts and avascular regions is computed manually from flat mounted retinas imaged using fluorescent probes that highlight the vascular network. However, such manual measurements are time-consuming and hampered by user variability and bias, thus a rapid and objective alternative is required.
Methods: We employ a machine learning approach to segment and characterize vascular tufts. The proposed quantitative retinal vascular assessment (QuRVA) technique uses quadratic discrimination analysis and morphological techniques to provide reliable measurements of vascular density and pathological vascular tuft regions, devoid of user intervention within seconds. Our algorithms allow also delineating the whole vasculature network, and identifying and analyzing avascular regions.
Results: Our first experiment shows the high degree of error and variability of manual segmentations. In consequence, we developed a set of algorithms to perform this task automatically. We benchmark and validate the results of our analysis pipeline using the consensus of several manually curated segmentations using commonly used computer tools. We describe the method, provide details for reproducing the algorithm, and validate all aspects of the analysis.
Conclusions: Manual and semi-automated procedures for tuft detection present strong fluctuations among users, demonstrating the need for fast and unbiased tools in this highly active research field with tremendous implications for basic research and industry.
