Automated Atlas-based Segmentation of Single Coronal Mouse Brain Slices using Linear 2D-2D Registration

TL;DR

This paper introduces an automatic method for segmenting 2D mouse brain slices by registering them to a 3D atlas using linear registration, improving accuracy and efficiency in histological data analysis.

Contribution

It presents a novel strategy for 2D-3D segmentation of brain slices using linear registration, addressing the gap between 2D histological data and 3D atlases.

Findings

Robustness validated across whole-brain scale

High accuracy in anatomical region identification

Efficient automatic segmentation process

Abstract

A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually, becoming therefore tedious and subjective. Another option is to use automatic or semi-automatic methods, among which segmentation using digital atlases co-registration. However, most available atlases are 3D, whereas digitized histological data are 2D. Methods to perform such 2D-3D segmentation from an atlas are required. This paper proposes a strategy to automatically and accurately segment single 2D coronal slices within a 3D volume of atlas, using linear registration. We validated its robustness and performance using an exploratory approach at whole-brain scale.