Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI

Automatic computer processing of large multidimensional images such as thos e produced by magnetic resonance imaging (MRI) is greatly aided by deformab le models, which are used to extract, identify, and quantify specific neuro anatomic structures. A general method of deforming polyhedra is presented h ere, with two novel features, First, explicit prevention of self-intersecti ng surface geometries is provided, unlike conventional deformable models, w hich use regularization constraints to discourage but not necessarily preve nt such behavior. Second, deformation of multiple surfaces with intersurfac e proximity constraints allows each surface to help guide other surfaces in to place using model-based constraints such as expected thickness of an ana tomic surface. These two features are used advantageously to identify autom atically the total surface of the outer and inner boundaries of cerebral co rtical gray matter from normal human MR images, accurately locating the dep ths of the sulci, even where noise and partial volume artifacts in the imag e obscure the visibility of sulci. The extracted surfaces are enforced to b e simple two-dimensional manifolds (having the topology of a sphere), even though the data may have topological holes, This automatic 3-D cortex segme ntation technique has been applied to 150 normal subjects, simultaneously e xtracting both the gray/white and gray/cerebrospinal fluid interface from e ach individual. The collection of surfaces has been used to create a spatia l map of the mean and standard deviation for the location and the thickness of cortical gray matter. Three alternative criteria for defining cortical thickness at each cortical location were developed and compared. These resu lts are shown to corroborate published postmortem and in vivo measurements of cortical thickness. (C) 2000 Academic Press.