On variational solutions for whole brain serial-section histology using the computational anatomy random orbit model

TL;DR

This paper introduces a variational framework for dense diffeomorphic atlas-mapping of whole brain histology stacks, addressing high-dimensionality and non-identifiability issues through joint optimization, demonstrated on mouse brain data.

Contribution

It proposes a novel joint variational approach combining rigid motion estimation and diffeomorphic mapping for histology stack alignment and atlas registration.

Findings

Successfully applied to mouse brain histology stacks

Addresses classical curvature non-identifiability

Demonstrates effective dense diffeomorphic atlas-mapping

Abstract

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 um 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 restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.