Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling

In cases of optic disc swelling, volumetric measurements and shape features are promising to evaluate the severity of the swelling and to differentiate the cause. However, previous studies have mostly focused on the use of volumetric spectral-domain optical coherence tomography (OCT), which is not always available in non-ophthalmic clinics and telemedical settings. In this work, we propose the use of a deep-learning-based approach (more specifically, an adaptation of a feature pyramid network, FPN) to obtain total-retinal-thickness (TRT) maps (as would normally be obtained from OCT) from more readily available 2D color fundus photographs. From only these thickness maps, we are able to compute both volumetric measures of swelling for quantification of the location/degree of swelling and 3D statistical shape measures for quantification of optic-nerve-head morphology. Evaluating our proposed approach (using nine-fold cross validation) on 102 paired color fundus photographs and OCT images (with the OCT acting as the ground truth) from subjects with various levels of optic disc swelling, we achieved significantly smaller errors and significantly larger linear correlations of both the volumetric measures and shape measures than that which would be obtained using a U-Net approach. The proposed method has great potential to make 3D ONH shape analysis possible even in situations where only color fundus photographs are available; these 3D shape measures can also be beneficial to help differentiate causes of optic disc swelling.