Automated TTC Image-Based Analysis of Mouse Brain Lesions

Small animals stroke models have widely been used to study the mechanisms of ischemic brain damage in controllable experimental settings. The evaluation of stroke lesions mainly relies on visual inspection of tissue samples collected after brain sectioning, slice staining and scanning, a procedure that is highly subjective and prone to human error. In this study we developed a machine-learning based methodology for automatic segmentation of lesions in mouse brain tissue samples, stained with Triphenyltetrazolium chloride (2% TTC). Our approach relies on the creation of a statistical mouse brain atlas of healthy TTC slices that was lacking in the literature. For this purpose we applied tissue clustering and Markov Random Fields (MRF) for brain tissue detection followed by deformable image registration for spatial normalization. The obtained statistical atlas is then exploited by outlier detection techniques and Random Forest classification to extract lesion probability maps in new slices. The good agreement between our segmentation results and expert-based lesion delineation on 12 mouse brains highlights the potential of the proposed approach to automate stroke volumetry analysis, thereby contributing to increased translational capacity of experimental stroke.