Deep-learning-based Segmentation of Fundus Photographs to Detect Central Serous Chorioretinopathy
Version 1We 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 segmentati
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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 segmentati