Probabilistic Inference in Computer Vision and Medical Imaging

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The objectives of the multidisciplinary research program proposed are to develop new probabilistic formulations for problems in computer vision applied in particularly challenging medical imaging contexts, such as those presented in neurology and neurosurgery, where they have the potential to lead to significant advancements in terms of robustness, accuracy or speed over traditional techniques. First, new sophisticated, multi-level probabilistic graphical models will be devised for a wide variety of open and difficult pathology detection and segmentation tasks in the domain of medical imaging. New, high level, irregular grid (i.e. non-lattice) random fields will be developed to properly represent contextual (spatial and textural) information. Inference will iterate across voxel, lesion and context level random fields until convergence. Development of new graphical models for these domains will require departures from popular neighbourhood models, innovation in design of appropriate models for node and edge information, as well as appropriate inference techniques. In addition to theoretical contributions machine learning, medical image analysis and to computer vision, the resulting models should significantly improve a wide variety of difficult and important pathology detection and segmentation tasks in real, clinical domains, such as Multiple Sclerosis lesion segmentation, the segmentation of sub-classes of brain tumours, and breast cancer detection. Through collaborations with a local company, my research team will have access to the largest dataset of real, multicenter, clinical trial, manually annotated, patient MRI images of any medical image analysis group in the world in which to test the methods.