EyeLiner: A Deep Learning Pipeline for Longitudinal Image Registration Using Fundus Landmarks.

ABSTRACT:

Objective

Detecting and measuring changes in longitudinal fundus imaging is key to monitoring disease progression in chronic ophthalmic diseases, such as glaucoma and macular degeneration. Clinicians assess changes in disease status by either independently reviewing or manually juxtaposing longitudinally acquired color fundus photos (CFPs). Distinguishing variations in image acquisition due to camera orientation, zoom, and exposure from true disease-related changes can be challenging. This makes manual image evaluation variable and subjective, potentially impacting clinical decision-making. We introduce our deep learning (DL) pipeline, "EyeLiner," for registering, or aligning, 2-dimensional CFPs. Improved alignment of longitudinal image pairs may compensate for differences that are

SUBMITTER: Veturi YA 

PROVIDER: S-EPMC11773051 | biostudies-literature | 2025 Mar-Apr

REPOSITORIES: biostudies-literature