Choroidal thickness estimation from colour fundus photographs by adaptive binarisation and deep learning, according to central serous chorioretinopathy status.

This study was performed to estimate choroidal thickness by fundus photography, based on image processing and deep learning. Colour fundus photography and central choroidal thickness examinations were performed in 200 normal eyes and 200 eyes with central serous chorioretinopathy (CSC). Choroidal thickness under the fovea was measured using optical coherence tomography images. The adaptive binarisation method was used to delineate choroidal vessels within colour fundus photographs. Correlation coefficients were calculated between the choroidal vascular density (defined as the choroidal vasculature appearance index of the binarisation image) and choroidal thickness. The correlations between choroidal vasculature appearance index and choroidal thickness were -0.60 for normal eyes (p < 0.01) and -0.46 for eyes with CSC (p < 0.01). A deep convolutional neural network model was independently created and trained with augmented training data by K-Fold Cross Validation (K = 5). The correlation coefficients between the value predicted from the colour image and the true choroidal thickness were 0.68 for normal eyes (p < 0.01) and 0.48 for eyes with CSC (p < 0.01). Thus, choroidal thickness could be estimated from colour fundus photographs in both normal eyes and eyes with CSC, using imaging analysis and deep learning.