Deep-learning-based Segmentation of Fundus Photographs to Detect Central Serous Chorioretinopathy

Published: 22 February 2022| Version 5 | DOI: 10.17632/4k64fwnp4k.5
TaeKeun Yoo

Description Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography, TVST, 2022 We developed a pix2pix deep learning model for segmentation of subretinal fluid area in fundus photographs to detect central serous chorioretinopathy (CSC). The total dataset included fundus photographs and a segmentation image dataset from 194 eyes with CSC from the medical centers and publicly accessible datasets. Additionally, we recruited 93 fundus photographs of the healthy eyes from the same center to build a classification model to discriminate CSC from normal retina. Manual segmentation is a tedious and time-consuming task that requires domain-specific knowledge. Initially, one ophthalmologist manually screened the fundus photography images with CSC. We asked three ophthalmologists including two licensed ophthalmologists (grader 1 & 2) and one ophthalmology resident (grader 3) to segment the entire SRF area in the retinal images. First, we prepared the dataset in Google Drive. Second, the users can upload the code file in Google Drive and open the file on the Google Drive page on the web browser. Third, prepare the dataset in Google Drive and match the folder location of the code with the address of the actual folders. In our experiment as an example, we saved the training dataset at "csc/segmentation/train/" and the test dataset at "csc/segmentation/test/" in our own Google Drive. Fourth, click the play button to the left of the code cell one by one. The second code cell links the datasets to this Colaboratory notebook using Google Drive. The manuscript of the study was accepted to TVST, ARVO journal.



Aerospace Medical Center


Health Sciences, Retina, Segmentation, Fundus Imaging