A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.

ABSTRACT: Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. The quantification of vessel tufts and avascular regions is typically computed manually from flat mounted retinas imaged using fluorescent probes that highlight the vascular network. Such manual measurements are time-consuming and hampered by user variability and bias, thus a rapid and objective method is needed. Here, we introduce a machine learning approach to segment and characterize vascular tufts, delineate the whole vasculature network, and identify and analyze avascular regions. Our quantitative retinal vascular assessment (QuRVA) technique uses a simple machine learning method and morphological analysis to provide reliable computations

SUBMITTER: Mazzaferri J 

PROVIDER: S-EPMC5834630 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature