Dataset Information

Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data.

ABSTRACT:

Purpose

To evaluate the performance of a federated learning framework for deep neural network-based retinal microvasculature segmentation and referable diabetic retinopathy (RDR) classification using OCT and OCT angiography (OCTA).

Design

Retrospective analysis of clinical OCT and OCTA scans of control participants and patients with diabetes.

Participants

The 153 OCTA en face images used for microvasculature segmentation were acquired from 4 OCT instruments with fields of view ranging from 2 × 2-mm to 6 × 6-mm. The 700 eyes used for RDR classification consisted of OCTA en face images and structural OCT projections acquired from 2 commercial OCT systems.