Detecting Cortical Surface Regions in Structural MR Data

We present a novel level-set method for evolving open surfaces embedded in three-dimensional volumes. We adapt the method for statistical detection and segmentation of cytoarchitectonic regions of the cortical ribbon (e.g., Brodmann areas). In addition, we incorporate an explicit interface appearance model which is oriented normal to the open surface, allowing one to model characteristics beyond voxel intensities and high gradients. We show that such models are well suited to detecting embedded cortical structures. Appearance models of the interface are used in two ways: firstly, to evolve an open surface in the normal direction for the purpose of detecting the location of the surface, and secondly, to evolve the boundary of the surface in a direction tangential to the surface in order to delineate the extent of a specific Brodmann area within the cortical ribbon. The utility of the method is demonstrated on a challenging ex-vivo structural MR dataset for detection of Brodmann area 17.