  We describe a diffeomorphic registration algorithm that allows groups of
images to be accurately aligned to a common space, which we intend to
incorporate into the SPM software. The idea is to perform inference in a
probabilistic graphical model that accounts for variability in both shape and
appearance. The resulting framework is general and entirely unsupervised. The
model is evaluated at inter-subject registration of 3D human brain scans. Here,
the main modeling assumption is that individual anatomies can be generated by
deforming a latent 'average' brain. The method is agnostic to imaging modality
and can be applied with no prior processing. We evaluate the algorithm using
freely available, manually labelled datasets. In this validation we achieve
state-of-the-art results, within reasonable runtimes, against previous
state-of-the-art widely used, inter-subject registration algorithms. On the
unprocessed dataset, the increase in overlap score is over 17%. These results
demonstrate the benefits of using informative computational anatomy frameworks
for nonlinear registration.
