An automated mapping method for Nissl-stained mouse brain histologic sections

Background: Histologic evaluation of the central nervous system is often a critical endpoint in in vivo efficacy studies, and is considered the essential component of neurotoxicity assessment in safety studies. Automated image analysis is a powerful tool that can radically reduce the workload associated with evaluating brain histologic sections. New method: We developed an automated brain mapping method that identifies neuroanatomic structures in mouse histologic coronal brain sections. The method utilizes the publicly available Allen Brain Atlas to map brain regions on digitized Nissl-stained sections. Results: The method's accuracy was first assessed by comparing the mapping results to structure delineations from the Franklin and Paxinos (FP) mouse brain atlas. Brain regions mapped from FP Nissl-stained sections and calculated volumes were similar to structure delineations and volumes derived from corresponding FP illustrations. We subsequently applied our method to mouse brain sections from an in vivo study where the hippocampus was the structure of interest. Nissl-stained sections were mapped and hippocampal boundaries transferred to adjacent immunohistochemically stained sections. Optical density quantification results were comparable to those from time-consuming, manually drawn hippocampal delineations on the IHC-stained sections. Comparison with existing methods: Compared to other published methods, our method requires less manual input, and has been validated comprehensively using a secondary atlas, as well as manually annotated brain IHC sections from 68 study mice. Conclusions: We propose that our automated brain mapping method enables greater efficiency and consistency in mouse neuropathologic assessments.