Restoring morphology of light sheet microscopy data based on magnetic resonance histology

Abstract

The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g. The Allen Brain Atlas (ABA), which limits the precision of measuring regional volumes and eliminates the individual difference in brain partitions for different strains, ages, and environmental exposures. Thus, these approaches obscure valuable anatomical information. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same brain restoring the morphology of the LSM images to the in-skull morphology. Our pipeline which maps 3D LSM data (terabyte level per dataset) to magnetic resonance histology of the same mouse brain provides accurate registration with low displacement error in 10 hours with limited manual input. Optimization and validation, from initialization of the data, designing the quantitative loss function, optimizing the 20+ image processing variables on multiple resolution scales, and finalizing the application on full resolution data has been integrated through a structured workflow. Excellent agreement has been seen between registered data and reference data both locally and globally. This workflow has been applied to a collection of datasets with varied MRH and LSM contrast providing a routine method for streamlined registration of LSM images to MRH.