Automated Atlas-based Segmentation of Single Coronal Mouse Brain Slices using Linear 2D-2D Registration
This addresses the tedious and subjective manual segmentation in brain histological data analysis for researchers, but it is incremental as it adapts existing atlas-based methods to 2D-3D contexts.
The paper tackles the problem of segmenting anatomical regions in 2D mouse brain slices by proposing an automated method using linear 2D-2D registration with a 3D atlas, validated for robustness and performance at whole-brain scale.
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.