Oct | 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

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

This is the summary publication of the AGE challenge on angle closure glaucoma detection from OCT scans of the anterior segment.

Huazhu Fu, Fei Li, Xu Sun, Xingxing Cao, Jingan Liao, José Ignacio Orlando, Xing Tao, Yuexiang Li, Shihao Zhang, Mingkui Tan, Chenglang Yuan, Cheng Bian, Ruitao Xie, Jiongcheng Li, Xiaomeng Li, Jing Wang, Le Geng, Panming Li, Huaying Hao, Jiang Liu, Yan Kong, Yongyong Ren, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

We introduced a fully automated approach to segment the photorceptor layer, evaluate its thickness and track potential disruptions using an ensemble of deep neural networks.

José Ignacio Orlando, Bianca S Gerendas, Sophie Riedl, Christoph Grechenig, Anna Breger, Martin Ehler, Sebastian M Waldstein, Hrvoje Bogunović, Ursula Schmidt-Erfurth

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina

We applied CycleGANs to reduce the covariate shift of models trained on one OCT vendor and evaluated on a different one. And it works quite well!

David Romo-Bucheli, Philipp Seeböck, José Ignacio Orlando, Bianca S. Gerendas, Sebastian M. Waldstein, U Schmidt-Erfurth, Hrvoje Bogunović

On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems

Anna introduced a new mathematical approach for dimensionality reduction that we incorporated into loss functions to augment target information and improve performance.

Anna Breger, José Ignacio Orlando, Pavol Harar, Monika Dörfler, Sophie Klimscha, Christoph Grechenig, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler

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

An Amplified-Target Loss Approach for Photoreceptor Layer Segmentation in Pathological OCT Scans

We introduce an augmented target loss function framework for photoreceptor layer segmentation that penalizes errors in the central area of each B-scan. It allows to significantly improve performance with respect to the standard loss functions.

José Ignacio Orlando, Anna Breger, Hrvoje Bogunović, Sophie Riedl, Bianca S. Gerendas, Martin Ehler, Ursula Schmidt-Erfurth

U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans

We developed a Bayesian U-Net model for photoreceptor layer segmentation in OCT that predicts epistemic uncertainty maps highlighting potential areas of error in the segmentation.

José Ignacio Orlando, Philipp Seeböck, Hrvoje Bogunović, Sophie Klimscha, Christoph Grechenig, Sebastian M. Waldstein, Bianca S. Gerendas, Ursula Schmidt-Erfurth

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

We used CycleGANs to translate OCT images from one vendor to another. This approach allows us to increase the performance of fluid segmentation models trained on one vendor and evaluated on another.

Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth

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