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.

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.

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!

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.

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.

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.

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.

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.

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.

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.