Deep-learning-based Segmentation of Fundus Photographs to Detect Central Serous Chorioretinopathy
Description
We developed a pix2pix deep learning model for segmentation of subretinal fluid area in fundus photographs to detect central serous chorioretinopathy (CSC). The dataset include fundus photographs and segmentation images from 105 eyes with CSC and 40 healthy eyes. We retrospectively reviewed the medical records and multimodal images of a total of 105 eyes of patients with had CSC at Severance Eye Hospital, Aerospace Medical Center, and publicly accessible databases. The reference segmentation for subretinal fluid area was performed manually by an expert ophthalmologist. First, the user should upload "pix2pix_csc_segmentation.ipynb" file in the Google drive. And open the file in the Google drive page. Second, please link the datasets to this colab notebook using Google drive. In our experiment, we save the training dataset at "csc/segmentation/seg_pix/" and the test dataset at "csc/segmentation/seg_test/". Third, run the codes in Google Colab by clicking buttons.