Age-Related Macular Degeneration | José Ignacio Orlando

Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning

We present ReSenseNet, a 3D to 2D deep neural network that is able to predict microperimetry maps from OCT images.

Philipp Seeböck, Wolf-Dieter Vogl, Sebastian M Waldstein, José Ignacio Orlando, Magdalena Baratsis, Thomas Alten, Mustafa Arikan, Georgios Mylonas, Hrvoje Bogunović, Ursula Schmidt-Erfurth

Multiclass Segmentation as Multitask Learning for Drusen Segmentation in Retinal Optical Coherence Tomography

We posed a multiclass segmentation task as a single multitask model with binary segmentation targets. Our results indicate that this approach might be useful to deal with “sandwiched” structures.

Rhona Asgari, José Ignacio Orlando, Sebastian M. Waldstein, Ferdinand Schlanitz, Magdalena Baratsits, Ursula Schmidt-Erfurth, Hrvoje Bogunović

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

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

We used epistemic uncertainty estimates to discover potential abnormalities in diseased OCT scans. The uncertainty maps are obtained by a Bayesian U-Net trained on healthy OCT scans with weak labels of the retinal layers.

Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl, Sebastian M. Waldstein, Hrvoje Bogunović, Sophie Klimscha, Georg Langs, Ursula Schmidt-Erfurth