Computer-assisted shape classification of middle cerebral artery aneurysms for surgical planning

We present a method for classifying the shape of middle cerebral artery (MCA) aneurysms using segmented surfaces from angiograms. The classification follows a set of visual criteria established by experienced surgeons to group aneurysms based on the clipping strategies used in surgery. Starting from a centerline representation of the input, our method automatically classifies the input into one of 4 types using a combination of graph analysis and supervised learning. When evaluated on a cohort of 84 subjects, our method achieves between 60% to 69% expected classification accuracy (p <; 10^-3) with zero to a moderate amount of input from novice users.