Research focus

We develop novel and effective technologies for the automated interpretation of medical imaging data to aid clinicians in the decision-making processes related to various aspects of modern health care such as disease diagnosis, prognosis, treatment planning and post-treatment monitoring and care. Our research activities run the gamut from exploring more accurate and efficient fundamental image processing algorithms to engineering big data, artificial intelligence (AI) pipelines for high-throughput medical image data processing. A hallmark of our research program is a strong emphasis on the translation of our research outputs for practical clinical use. We therefore collaborate with clinicians and medical researchers world-wide to conduct large-scale epidemiological studies, in which we employ the advanced medical image analysis tools developed by our lab to discover biomarkers and underlying mechanisms of different diseases.

Specifically, we focus on developing automated medical image analysis pipelines, which enable the delivery of improved health care in the domains of neurological (brain) diseases (e.g., Alzheimer’s disease (AD), Parkison’s disease (PD), and fronto-temporal dementia (FTD)) and eye/retinal diseases (e.g., Glaucoma, Age-related Macular dengeneration (AMD) and Diabetic Retinopathy (DR)). To this end, we have compiled very large databases of magnetic resonance imaging (MRI), positron emission tomography (PET), and diffusion tensor imaging (DTI) images of the human brain along with optical coherence tomography (OCT) images of the human retina. With access to these multi-modal imaging datasets, we develop robust and automated tools that extract desease-relevant features and build predictive models for disease diagnosis using advanced machine learning techniques.