An automated template-based adaptive threshold approach for measuring ventricular volume enlargement in mouse brain MR microscopy.

Abstract

Mouse models for human diseases play an important role in developing therapeutic measures and understanding disease development and mechanisms. Ventricular enlargement is an objective and sensitive biomarker of neuropathological change associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Thus, imaging based volumetric measures of mouse brain ventricle can be used as a biomarker for detecting and monitoring pathology. However, till now most mouse ventricular segmentation is still based on manual tracing, and current region of interests (ROIs) labelling approaches on mouse brain don't work well on small brain structure like ventricle. In this paper, an automated template-based method was developed to evaluate for the identification of a ventricular ROIs in mouse brain imaging studies. Monte Carlo simulation was applied to evaluate the efficiency of this approach in detecting computer-simulated ventricle in laboratory mice. The method demonstrated a satisfactory performance with consistent high correlations between the detected and simulated volume changes. This approach can be used to investigate the ventricular volume changing in transgenic mice and other putative animal models of AD.