Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers.

ABSTRACT:

Purpose

To develop a fully-automatic hybrid algorithm to jointly segment and quantify biomarkers of polypoidal choroidal vasculopathy (PCV) on indocyanine green angiography (ICGA) and spectral domain-OCT (SD-OCT) images.

Design

Evaluation of diagnostic test or technology.

Participants

Seventy-two participants with PCV enrolled in clinical studies at Singapore National Eye Center.

Methods

The dataset consisted of 2-dimensional (2-D) ICGA and 3-dimensional (3-D) SD-OCT images which were spatially registered and manually segmented by clinicians. A deep learning-based hybrid algorithm called PCV-Net was developed for automatic joint segmentation of biomarkers. The PCV-Net consisted of a 2-D segmentation branch for ICGA and 3-D segmentation branch for SD-OCT. We dev

SUBMITTER: Loo J 

PROVIDER: S-EPMC10070921 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature