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.
Diffeomorphic Upsampling of Serially Acquired Sparse 2D Cross-Sections in Cardiac MRI
We design diffeomorphic techniques to estimate unobserved data between sparsely sampled slices in cardiac MRI.
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.
A community-developed open-source computational ecosystem for big neuro data
A series of open source and web accessible resources for studying neuroscience related data.