Characterization of Retinal Arteries by Adaptive Optics Ophthalmoscopy Image Analysis

This paper aims at quantifying biomarkers from the segmentation of retinal arteries in adaptive optics ophthalmoscopy images (AOO).

The segmentation is based on the combination of deep learning and knowledge-driven deformable models to achieve a precise segmentation of the vessel walls, with a specific attention to bifurcations. Biomarkers (junction coefficient, branching coefficient, wall to lumen ratio ( wlr)) are derived from the resulting segmentation.

reliable and accurate segmentations ( mse = 1.75 ±1.24 pixel) and measurements are obtained, with high reproducibility with respect to images acquisition and users, and without bias.

In a preliminary clinical study of patients with a genetic small vessel disease, some of them with vascular risk factors, an increased wlr was found in comparison to a control population.

The wlr estimated in AOO images with our method (AOV, Adaptive Optics Vessel analysis) seems to be a very robust biomarker as long as the wall is well contrasted.