On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

ABSTRACT: This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology

SUBMITTER: Lee BC 

PROVIDER: S-EPMC6324828 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature