Quantitative characterization of retinal features in translated OCTA.

ABSTRACT: This study explores the feasibility of quantitative Optical Coherence Tomography Angiography (OCTA) features translated from OCT using generative machine learning (ML) for characterizing vascular changes in retina. A generative adversarial network framework was employed alongside a 2D vascular segmentation and a 2D OCTA image translation model, trained on the OCT-500 public dataset and validated with data from the University of Illinois at Chicago (UIC) retina clinic. Datasets are categorized by scanning range (Field of view) and disease status. Validation involved quality and quantitative metrics, comparing translated OCTA (TR-OCTA) with ground truth OCTAs (GT-OCTA) to assess the feasibility for objective disease diagnosis. In our study, TR-OCTAs showed high image quality in both 3 and 6 

SUBMITTER: Badhon RH 

PROVIDER: S-EPMC11537946 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature