Characterization of Retinal Arteries by Adaptive Optics Ophthalmoscopy Image Analysis.

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

Methods: 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.

Results: 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.

Significance: 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.

Conclusion: 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.