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Linking Function and Structure: Prediction of Retinal Sensitivity in AMD from OCT using Deep Learning
We propose a deep learning methodology to predict retinal sensitivity from OCT volumes.
Philipp Seeböck, Wolf-Dieter Vogl, Sebastian M Waldstein, Magdalena Baratsits, José Ignacio Orlando, Thomas Alten, Hrvoje Bogunovic, Mustafa Arikan, Georgios Mylonas, Ursula Schmidt-Erfurth
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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.
José Ignacio Orlando, João Barbosa Breda, Karel Van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant
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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.
José Ignacio Orlando, Marcos Fracchia, Valeria Del Río, Mariana Del Fresno
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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!
José Ignacio Orlando, Elena Prokofyeva, Mariana Del Fresno, Matthew B. Blaschko
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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.
José Ignacio Orlando, Matthew B. Blaschko
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