Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning.

ABSTRACT:

Purpose

Automated segmentation software in optical coherence tomography (OCT) devices is usually developed for and primarily tested on common diseases. Therefore segmentation accuracy of automated software can be limited in eyes with rare pathologies.

Methods

We sought to develop a semisupervised deep learning segmentation model that segments 10 retinal layers and four retinal features in eyes with Macular Telangiectasia Type II (MacTel) using a small labeled dataset by leveraging unlabeled images. We compared our model against popular supervised and semisupervised models, as well as conducted ablation studies on the model itself.

Results

Our model significantly outperformed all other models in terms of intersection over union on the 10 retinal layers and two retinal

SUBMITTER: Verma A 

PROVIDER: S-EPMC11542501 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature