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

Lee, B. C., Tward, D. J., Mitra, P. P., Miller, M. I. (December 2018) On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model. PLoS Comput Biol, 14 (12). e1006610. ISSN 1553-734x

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URL: https://www.ncbi.nlm.nih.gov/pubmed/30586384
DOI: 10.1371/journal.pcbi.1006610

Abstract

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 mum 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.

Item Type: Paper
Subjects: Investigative techniques and equipment > brain atlas
CSHL Authors:
Communities: CSHL labs > Mitra lab
Depositing User: Matthew Dunn
Date: 26 December 2018
Date Deposited: 07 Jan 2019 16:44
Last Modified: 04 Mar 2019 20:46
PMCID: PMC6324828
Related URLs:
URI: http://repository.cshl.edu/id/eprint/37536

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