Shape-based cortical surface segmentation for visualization brain mapping.

Abstract

We describe a knowledge-based approach to cortical surface segmentation that uses learned knowledge of the overall shape and range of variation of the cortex (excluding the detailed gyri and sulci) to guide the search for the grey-CSF boundary in a structural MRI image volume. The shape knowledge is represented by a radial surface model, which is a type of geometric constraint network (GCN) that we hypothesize can represent shape by networks of locally interacting constraints. The shape model is used in a protocol for visualization-based mapping of cortical stimulation mapping (CSM) sites onto the brain surface, prior to integration with other mapping modalities or as input to existing surface analysis and reconfiguration programs. Example results are presented for CSM data related to language organization in the cortex, but the methods should be applicable to other situations where a realistic visualization of the brain surface, as seen at neurosurgery, is desired.