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Common operations applied to brain images involve various forms of image registration and segmentation. Often, these are considered as ad hoc steps within some form of processing pipeline, with little or no attempt to consolidate or generalise the principles. A more rigorous approach involves formulating all these operations within a unifying probabilistic generative model of large collections of image data.
Of particular importance to brain imaging is the incorporation of spatial deformations within the model, along with methods for encoding inter-subject variability. Image datasets are large and models are complex, so numerous approximations are necessary - although a continuation of Moore's Law may allow more principled solutions in future. In general, those models that most accurately learn and encode real biological phenomena (basic science) are likely to be the most useful for making predictions on which real-world decisions may be made (applied science). In addition to addressing the immediate short term needs of neuroimagers, the aim is to also lay the groundwork for a future in which harnessing the power of extremely large image datasets becomes the norm.
Current projects include developing a mixed effects modelling framework assessing longitudinal volumetric changes to brain anatomy, and a framework to learn tissue probability atlases from large image datasets.
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