In the field of neuroimaging, Bayesian modelling techniques have been largely adopted
and recognised as powerful tools for the purpose of extracting quantitative anatomical
and functional information from medical scans. Nevertheless the potential of Bayesian
inference has not yet been fully exploited, as many available tools rely on point estimation
techniques, such as maximum likelihood estimation, rather than on full Bayesian
inference.
The aim of this thesis is to explore the value of approximate learning schemes, for
instance variational Bayes, to perform inference from brain and spinal cord MRI data.
The applications that will be explored in this work mainly concern image segmentation
and atlas construction, with a particular emphasis on the problem of shape and intensity
prior learning, from large training data sets of structural MR scans.
The resulting computational tools are intended to enable integrated brain and spinal
cord morphometric analyses, as opposed to the approach that is most commonly adopted
in neuroimaging, which consists in optimising separate tools for brain and spine morphometrics
