Spatiotemporal 3D image registration for mesoscale studies of brain development

Sergey Shuvaev,Konstantin Anokhin,Alexei A Koulakov,Roman Kiryanov,Grigori Enikolopov,Alexander Lazutkin

doi:10.1038/s41598-022-06871-8

Abstract

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we introduce CORGI (Customizable Object Registration for Groups of Images), an algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithm’s sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align (“synchronize”) brain samples in time, accounting for individual development paces. We tested CORGI on 28 samples of whole-mounted perinatal mouse brains (P0–P9) and compared its accuracy with other registration algorithms. Our algorithm offers a runtime of several minutes per brain on a laptop and automates such brain registration tasks as mapping brain data to atlases, comparing experimental groups, and monitoring brain development dynamics.

Highlights

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates
The focus of this work was on designing algorithms for reconstructing the developmental dynamics of the perinatal mouse brain via the registration of brain samples in space and time
To evaluate the performance of the four algorithms (CORGI, ClearMAP, CUBIC, CUBIC-reduced), we have visually inspected the aligned brain samples to check the match of the brain regions which we have identified as problematic for automated registration

Summary

Introduction

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. The available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we introduce CORGI (Customizable Object Registration for Groups of Images), an algorithm for the registration of perinatal brains. Instead of aligning handpicked features, free-form registration methods optimize spatial transformation to maximize the similarity between two images. Following Sederberg and Parry[7], one may conceptualize free-form registration as deforming a brain sample together with the transparent agarose cube in which it is embedded to increase similarity integrated over the entire image. Alignment is typically preceded by image s moothing[9] and involves converting raster images to vector fields[10]

Methods

Results

Conclusion