Deep learning based retinal hard exudates quantification of optical coherence tomography.

ABSTRACT:

Purpose

To develop a deep learning (DL) model for segmenting retinal hard exudates (HE) from optical coherence tomography (OCT) scans.

Methods

A modified U-Net architecture was trained on manually segmented OCT B-scans of retinal HE. The training set included 1,811 OCT scans from 15 patients with diabetic retinopathy or branch retinal vein occlusion. The model was evaluated using Dice coefficient and accuracy in idependant test set, and its HE area and volume predictions were compared to manually measured HE areas from a previous clinical study. Additionally, a 2D projected image was generated from the 3D structure of the predicted HE.

Results

The DL model achieved a Dice coefficient of 0.721 and an accuracy of 99.9% on the test set. There was a moderate correlation b

SUBMITTER: Yoon CK 

PROVIDER: S-EPMC12535103 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature