Characterization of retinal arteries by adaptive optics ophthalmoscopy image analysis

• Published in: IEEE transactions on biomedical engineering Vol. PP; pp. 1 - 10
• Main Authors: Rossant, F., Bloch, I., Trimeche, I., Bellescize, J.-B. de Regnault de, Farias, D. Castro, Krivosic, V., Chabriat, H., Paques, M.
• Format: Journal Article
• Language: English
• Published: IEEE 03.06.2024
• Subjects: Adaptive optics ophthalmoscopy; Arteries; Bifurcation; Biomarkers; deep learning; deformable models; Diseases; Image segmentation; Imaging; Retina; retina vessel walls segmentation
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3408232

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.