Solving the where problem in neuroanatomy: a generative framework with learned mappings to register multimodal, incomplete data into a reference brain

We developed a generative algorithm for registration of micron resolution serial section microscopy images to the Allen reference atlas.

Estimating diffeomorphic mappings between templates and noisy data: Variance bounds on the estimated canonical volume form

We derived the Cramer Rau bound for estimating volume changes from deformable image registration, which is a lower bound on the variance of an estimator. We demonstrated its implications for image registration performance in asymmetric methods versus symmetric methods, the former generally performing better.