Simple Code Implementation for Deep Learning-Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography.

ABSTRACT:

Purpose

Central serous chorioretinopathy (CSC) is a retinal disease that frequently shows resolution and recurrence with serous detachment of the neurosensory retina. Here, we present a deep learning analysis of subretinal fluid (SRF) lesion segmentation in fundus photographs to evaluate CSC.

Methods

We collected 194 fundus photographs of SRF lesions from the patients with CSC. Three graders manually annotated of the entire SRF area in the retinal images. The dataset was randomly separated into training (90%) and validation (10%) datasets. We used the U-Net segmentation model based on conditional generative adversarial networks (pix2pix) to detect the SRF lesions. The algorithms were trained and validated using Google Colaboratory. Researchers did not need prior knowledge of

SUBMITTER: Yoo TK 

PROVIDER: S-EPMC8842634 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature