Probabilistic inference in computer vision and medical imaging

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Advances in medical imaging have permitted a large amount of information to be made available to medical experts. However, in many cases, the information is difficult to use for guidance and diagnosis without further interpretation. Image-guided neurosurgery systems (IGNS), for example, have provided a wealth of patient-specific pre-operative images (e.g. MRI, fMRI) to the surgeon during the procedure to use for planning and guidance. However complex brain movements during open-skull operations reduce the effectiveness of using these images for guidance. The objectives of the proposed research program are to develop new probabilistic frameworks in computer vision for a wide range of medical contexts, where they have the potential to lead to concrete improvements in medical diagnosis and patient care. Specifically, we will begin by developing probabilistic medical image registration tools that will permit us to quantify the uncertainties in the resulting interpretation. In the context of neurosurgery, this will permit us to match tracked intra-operative images (e.g. ultrasound) to pre-operative images in order to quantify and correct for brain deformations, and to communicate to the surgeon where the system has confidence in the interpretation. This leads to the first active image-guided neurosurgery system (AIGNS) where visual feedback will permit a surgeon to optimize placement and use of intra-operative imaging systems to reduce ambiguities in the interpretation. Clinical benefits include faster and more accurate surgical procedures, and reductions in patient trauma and hospital costs. We will also apply the framework to breast cancer radiotherapy, with the goal of attaining more accurate radiation treatment of lumpectomy (resected tumour) sites. Finally, this proposal aims to develop new probabilistic frameworks to automatically learn the local statistical variability of image patterns from large datasets of brain images, potentially leading to breakthroughs in our understanding of the natural variability of healthy human brains and to discoveries of new biomarkers of neurological diseases (e.g. Alzheimer's).