Automated Cerebellar Lobule Segmentation using Graph Cuts

The cerebellum is important in coordinating many vital functions including speech, motion, and eye movement. Accurate delineation of sub-regions of the cerebellum, into cerebellar lobules, is needed for studying the region specific decline in function from cerebellar pathology. In this work, we present an automated cerebellar lobule segmentation method using graph cuts, with a region-based term enforcing consistency with multi-atlas labeling results, and a boundary term defined by membership output from a random forest classifier. The region-based term ensures that the location of the lobules conforms to the anatomical convention encoded in the training subjects. The boundary term ensures that the segmentation follows the fine details of lobule boundaries in the subject image. We compared our method to both manual segmentations and a state-of-the-art multi-atlas label fusion technique.