Brains behind the eyes: Interpreting Medical Images

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Motivation: Modern medical imagers generate detailed images providing an unprecedented view of the internal anatomy. However, these machines are essentially sophisticated ‘cameras’ or ‘eyes’. Though they generate pretty pictures, they cannot quantify or ‘understand’ these images. The clinicians typically examine these images visually and qualitatively since tools for making accurate measurements from these images are not yet available in the clinic. Developing the ‘brains’ behind the ‘eyes’, or the intelligent algorithms behind the images that can convert raw imaging data into measurements that can be used to detect the onset of disease, diagnose a disease with confidence, or to quantitatively monitor disease progression is the motivation and the long term goal of my research program. Focus: My research program focuses on developing a computational anatomy framework to mine the 3D brain magnetic resonance (MR) images for biomarkers related to brain diseases such as Alzheimer’s disease (AD). A second focus is to develop a novel computational anatomy framework for interpreting 3D optical coherence tomography (OCT) images of the retina for biomarkers related to eye diseases such as age-related macular degeneration (AMD) or glaucoma. Since the retina is a neurosensory extension of the brain, biomarkers from retinal images are not only valuable for improving vision care, but in the future, may also serve as surrogates for brain biomarkers and potentially help in diagnosing or monitoring the progression of brain diseases. Challenges: Each 3D image typically has millions of samples and measurements. The signal of interest i.e. the changes due to disease, are typically localized in a subset of the voxels that make an image (such as the hippocampus, where memory circuits reside, or the optic nerve head, where optic nerve fibers exit the eye). Automated identification of regions of interest (segmentation) in large 3D volumetric images is a challenging and important task for all downstream measurements depend on this first task. Once segmented, each vector of morphometric measurements taken from a region of interest (ROI) is a point in a high-dimensional space. Distinguishing the signal (the changes in a ROI due to a disease or condition) from the ‘spread’, or variability due to normal variation in the ROI in the population, is a second key challenge. Research goals: are to increase accuracy in automated segmentation, registration and measurements, design better separable features and classifiers leading to the discovery of novel quantitative biomarkers for disease diagnosis and progression. Significance: Brain and eye diseases are the primary cause of disability among Canadians over the age of 65. They lead to poor quality of life for the individual and their family, and cost Canada billions of dollars each year. The proposed frameworks have the potential fill a major, unmet need in quantitative biomedical image interpretation technologies, help better utilize information present in imaging data, save valuable clinician time, and thus allow better quality health care to be delivered at lower cost. Progress: The past Discovery grant cycle has been highly productive for my lab, with 25 high-impact journal publications and 17 peer-reviewed conference publications. I was awarded the prestigious Michael Smith Foundation for Health Research Scholar award and the Association of Professional Engineers & Geoscientists of BC’s Meritorious Achievement award for my contributions to designing clinically applicable computational tools. Building on these strengths, I have created an exciting research program towards new discoveries in quantitative biomedical image interpretation in which I supervised 36 trainees in the past cycle.