Sulcal curve extraction using Laplace Beltrami eigenfunction level sets

Abstract

The complexity of the human cortex is demonstrated in the intricate pattern of gyri and sulci that arise from the cortical folding process during development. Quantitative assessment of cortical folding is important in the definition of normal brain development and provides insight into neurodevelopmental disorders. In this work, a method for sulcal curve extraction is proposed that combines the advantages of previously proposed depth based and curvature based methods. The technique, derived from Laplace Beltrami eigenfunction level sets, maps mean curvature on the level sets, and incorporates depth information using extracted sulci and gyri, a characteristic previously attributed only to depth based methods. The use of Laplace Beltrami eigenfunction level sets requires neither definition of an outer hull surface nor correspondence between the cortical surface and outer hull, both of which are required by depth based methods. The utility of the method for extracting sulcal curves is demonstrated by application to fetal sheep brain MRI data, imaged at key time points during development.