Choroid automatic segmentation and thickness quantification on swept-source optical coherence tomography images of highly myopic patients.

ABSTRACT:

Background

Thinning of the choroid has been linked with various ocular diseases, including high myopia (HM), which can lead to visual impairment. Although various artificial intelligence (AI) algorithms have been developed to quantify choroidal thickness (ChT), few patients with HM were included in their development. The choroid in patients with HM tends to be thinner than that of normal patients, making it harder to segment. Therefore, in this study, we aimed to develop and implement a novel deep learning algorithm based on a group-wise context selection network (GCS-Net) to automatically segment the choroid and quantify its thickness on swept-source optical coherence tomography (SS-OCT) images of HM patients.

Methods

A total of 720 SS-OCT images were obtained from 40 HM ey

SUBMITTER: Li M 

PROVIDER: S-EPMC9263793 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature