Brain meeting: Yael Balbastre

Brain meeting

Probabilistic inference consists of estimating a probability distribution based on a limited number of randomly sampled observations. When these observations are images, Euclidean inference (assuming no prior covariance among voxels) often fails to estimate a representative distribution of the data. This problem can be overcome by accounting for two characteristics of images: first, their intrinsic smoothness, which is captured by a local covariance among voxels; and second, their topology, which captures the fact that the objects represented in the images are invariant under some families of transformations (e.g., multiplicative or additive changes of appearance, affine or non-linear spatial deformations).
In this talk I will show that a set of images can be described by a mean and a distribution of transformations (of a given type), such that a single transformation from the distribution would map the mean image to a sample from the set of images, and that the particular transformation type depends on the nature of the variability to be modelled. I will show two practical applications capitalising on this framework: the estimation of sensitivity fields in multi-coil MR acquisitions, and the estimation of brain templates in computational anatomy. I will then show that by extending the model of prior covariance from capturing local smoothness only, to having a non-stationary form, more structured deviations from the mean image can be captured. This concept will be applied to the estimation of shape and appearance variability in the human brain.

There will be coffee, tea and cake in the conservatory directly after the talk.