Modeling Longitudinal Optical Coherence Tomography Images for Monitoring and Analysis of Glaucoma Progression

Area of Science:

• Ophthalmology
• Medical Imaging
• Biostatistics

Background:

• Glaucoma is a leading cause of irreversible blindness, characterized by progressive visual field loss.
• Early detection and monitoring of glaucoma progression are crucial for preserving vision.
• Current clinical monitoring relies on optical coherence tomography (OCT) but lacks a consensus on key biomarkers and quantitative 3D models of change over time.

Purpose of the Study:

• To develop a novel algorithm for quantitative modeling of 3D structural changes in glaucoma progression using longitudinal OCT data.
• To establish a hierarchical geodesic modeling approach to analyze subject-wise and population-level trajectories of ocular structures.

Main Methods:

• The study presents an algorithm performing hierarchical geodesic modeling on 3D OCT images.
• Subject-wise trajectories are modeled as geodesics in the space of diffeomorphisms.
• Population-level trajectories (glaucoma vs. control) are also modeled as geodesics, explaining individual variations.

Main Results:

• Preliminary experiments show significantly greater structural changes in glaucoma patients compared to normal aging.
• The algorithm quantifies the magnitude and patterns of 3D structural deterioration over time.

Conclusions:

• The developed algorithm provides a quantitative model for analyzing glaucoma progression from 3D OCT data.
• This approach has potential applications in patient-specific monitoring and understanding population trends in glaucoma.
• It addresses the need for a robust statistical model to analyze longitudinal OCT data for glaucoma research.