This is the summary publication of the AGE challenge on angle closure glaucoma detection from OCT scans of the anterior segment.
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.
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!
Anna introduced a new mathematical approach for dimensionality reduction that we incorporated into loss functions to augment target information and improve performance.
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.
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.
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.
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.
We propose a deep learning methodology to predict retinal sensitivity from OCT volumes.
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.