Abstract
The study of subjects with acquired brain damage in a specific location is important in exploring human brain function. Description of lesion locations within and across subjects is a crucial methodological component that usually involves the distinction of normal from damaged tissue (lesion segmentation) in relation to lesion locations in terms of a standard anatomical reference space (lesion mapping). Our study provides an atlas-based, computer-aided methodology for classification of hyperintense regions on diffusion-weighted images of the brain, representing either ischemic lesions or susceptibility artifacts. We applied a leave-one-out method of cross-validation that computed probabilistic atlases of true lesions and artifacts, based on training data. Our approach accurately classifies lesions and artifacts, but leaves a significant number of regions unclassified, due to the relatively small number of training samples. An initial segmentation step based on a larger sample of data sets is required to automate discrimination of lesions and artifacts.