Recent publications

Purpose: In this paper we propose to apply generative adversarial neural networks trained with a cycle-consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from Computed Tomography (CT) scans. Methods: A ray-casting US simulation approach is used to generate intermediate synthetic images from abdominal CT scans. Then, an unpaired set of these synthetic and real US images is used to train CycleGANs with two alternative architectures for the generator, a U-Net and a ResNet. These networks are finally used to translate ray-casting based simulations into more realistic synthetic US images. Results: Our approach was evaluated both qualitatively and quantitatively. A user study performed by two experts in US imaging shows that both networks significantly improve realism with respect to the original ray-casting algorithm (p << 0.001), with the ResNet model performing better than the U-Net. Conclusion: Applying CycleGANs allows to obtain better synthetic US images of the abdomen. These preliminary results pave the way towards efficient patient-specific US simulation for low-cost training of medical doctors and radiologists.
In CARS 2019

In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations of the photoreceptor layer and produces pixel-wise epistemic uncertainty maps that highlight potential areas of pathologies or segmentation errors. We empirically evaluated this approach in two sets of pathological OCT scans of patients with age-related macular degeneration, retinal vein oclussion and diabetic macular edema, improving the performance of the baseline U-Net both in terms of the Dice index and the area under the precision/recall curve. We also observed that the uncertainty estimates were inversely correlated with the model performance, underlying its utility for highlighting areas where manual inspection/correction might be needed.
In ISBI 2019

Glaucoma is the leading cause of irreversible but preventable blindness in the world. Its major treatable risk factor is the intra-ocular pressure, although other biomarkers are being explored to improve the understanding of the pathophysiology of the disease. It has been recently observed that glaucoma induces changes in the ocular hemodynamics. However, its effects on the functional behavior of the retinal arterioles have not been studied yet. In this paper we propose a first approach for characterizing those changes using computational hemodynamics predicted from patient specific retinal arborizations. The retinal blood flow is simulated using a 0D model for a steady, incompressible non Newtonian fluid in rigid domains. The simulation is performed on patient-specific arterial trees extracted from fundus images. We also propose a novel feature representation technique to comprise the outcomes of the simulation stage into a fixed length feature vector that can be used for classification studies. Our experiments on a new database of fundus images show that our approach is able to capture representative changes in the hemodynamics of glaucomatous patients. Code and data is available in this website.
In MICCAI 2018


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Our two papers on automated retinal OCT image analysis using deep learning have been accepted for presentation at ISBI 2019.


I presented our work on machine learning for ophthalmic image analysis at University of Buenos Aires.


REFUGE as part of OMIA, at MICCAI 2018


Our paper with João Barbosa Breda, Karel van Keer, Matthew B. Blascko, Pablo J. Blanco and Carlos A. Bulant has been accepted for presentation in MICCAI 2018.


My application to the NVIDIA Hardware Grant was successful!



I have been a Teaching Assistant in the following courses at UNICEN (Argentina):


Feel free to contact me if you have any questions about my research!