A probabilistic ribbon model for shape analysis of the cerebral sulci: Application to the central sulcus

Purpose: An approach for quantifying the shapes of the cerebral sulci is presented, utilizing a probabilistic geometric model, and it is applied to the central sulcus. Method: The geometric structure of the central sulcus is determined from a set of outlines on cross-sectional images and is used by a procedure that automatically labels the major crest lines, i.e., curves of locally maximal curvature, along the sulcus. An automated procedure then determines a parametric representation of the central sulcus that is consistent across individuals, in that it assigns the same parametric coordinates to corresponding regions of the sulcus. Results: The method is applied to the central sulci from 20 subjects. The use of this shape representation in cortical morphometric analysis applications is demonstrated, in particular in obtaining local depth and curvature measurements of a sulcus as well as in determining average shapes and variability. Conclusion: With this method, we were able to build parametric representations of the sulcal ribbons by preserving anatomical homologies.