Novel optimization strategies for medical image analysis

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Anatomical and functional medical imaging modalities, e.g. MRI and PET, are allowing physicians to peer inside the human body and observe a wealth of data crucial for understanding, diagnosing and treating diseases. The volume of medical data acquired is growing rapidly. The dimensionality of images has increased from 2D scalar images to dynamic 3D multi-valued fields. This is resulting in image data that cannot be effectively processed with traditional visual inspection. Therefore, automated computational tools for medical image analysis (MIA) are becoming indispensable in modern healthcare systems. The three most important and ubiquitous MIA tasks are image segmentation, registration, and shape analysis, which constitute the crux of image interpretation and quantification tasks. Segmentation is the process of identifying regions of interest in an image (e.g. to measure wall thickness of the myocardium), whereas registration is the process of finding meaningful correspondence between images (e.g. to compare across subjects or time). Shape analysis captures geometric and topological properties and reveals crucial information about disease stages, treatment progress, or growth rates. Despite numerous advances in these areas in the past few decades, accurate and automatic MIA algorithms continue to defy solution. The vast majority of these algorithms rely on solving optimization problems. However, very little work has been devoted to evaluating the appropriateness of the objective functions being optimized or to integrating high-level domain knowledge into the process. This proposal will focus on studying formal approaches for evaluating objective functions and designing them from the outset using rigorous mathematical and computational techniques. This research will also complement low-level optimization techniques with high-level, knowledge-driven MIA strategies using a novel artificial life framework. The goal is to ensure higher accuracy of automated MIA algorithms, in order to advance computer-aided diagnosis, computer-assisted interventions, and the many other aspects of healthcare that rely on medical imaging.