Multimodal Cross-registration and Quantification of Metric Distortions in Whole Brain Histology of Marmoset using Diffeomorphic Mappings
Brian C. Lee, Meng Kuan Lin, Yan Fu, Junichi Hata, Michael I. Miller,, Partha P. Mitra

TL;DR
This paper introduces a method to quantify and correct for geometric distortions in whole brain histology of marmosets by using diffeomorphic mappings guided by multimodal MRI, enabling accurate 3D reconstructions.
Contribution
It presents a novel computational approach for multimodal MRI-guided histological reconstruction and systematic quantification of tissue processing distortions in brain mapping.
Findings
Tape-transfer histology has minimal scale distortion (~2%)
Perfusion causes larger scale distortions (~7%)
First systematic quantification of local metric distortions in whole-brain histology
Abstract
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified the distortions in brain geometry from in-vivo to ex-vivo brains due to the tissue processing, which will be important when computing properties such as local cell and process densities at the voxel level in creating reference brain maps. Further, existing approaches focus on registering uni-modal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research, it is necessary to cross-register multi-modal data sets including MRIs and multiple histological series that can help address individual variations in brain architecture. Here we present a computational approach for same-subject multimodal MRI…
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Taxonomy
TopicsMorphological variations and asymmetry · Spectroscopy and Chemometric Analyses · Genetic and phenotypic traits in livestock
