2D rodent brain extraction using shape model and template learning

Abstract

Accurate rodent brain extraction is the basic step for many translational study using MR imaging. This report presents a template based approach to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we encode the rodent brain position into the search space to reliably locate the rodent brain and estimate the rough segmentation. With the initial mask, a level-set segmentation and a mask-based template learning are implemented further in the brain region. The fusion of the experts is used to generate a new mask. We finally combine the region growing based on the histogram distribution learning to delineate the final brain mask. Tested on a public data set, we achieved favorable results in both the automatic brain localization and segmentation.