Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images

We designed a method to summarize hemodynamic parameters obtained by 0D simulations so that they can be applied for glaucoma detection. We observed certain correlation between glaucoma and these hemodynamic features.

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

We developed a simple linear regression model that is able to estimate the hyperparameters of a fully-connected CRF model for blood vessel segmentation in fundus images.

Convolutional neural network transfer for automated glaucoma identification

We use pretrained VGG-S and OverFeat architectures as feature extractors for glaucoma detection in fundus pictures. We were able to get almost 0.8 AUC without fine-tuning the networks!

Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images

We introduced a discriminatively trained fully-connected conditional random field model for blood vessel segmentation in retinal images.