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NORHA is a novel index for quantifying hippocampal asymmetries in neurodegenerative conditions. It shows promise as a biomarker for detecting unilateral abnormalities, such as hippocampal sclerosis, and correlates positively with the functional cognitive test CDR-SB, indicating its potential in dementia diagnosis.

We developed a new set of ICL sizing formulae based on LASSO regression, OCT measurements and ocular biometry data.

We developed a deep learning approach that leverages vessel segmentations from color fundus photographs to improve FAZ segmentation in FA images.

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

We develop a deep neural network that automatically extracts contextual features from patches in sketches, trained with 3D models rendered with non-photorealistic techniques. Our method is able to find dense correspondences between real world sketches!

We developed a deep learning based pipeline to segment the lumen boundary in IVUS datasets based on multiframes inputs.

We used SVMs to filter out false positive detections causes by single subject VBM when applied for discovering abnormalities in MRI scans.

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!