Sure-fit: an automated method for modeling the shape of cerebral cortex and other complex structures using customized filters and transformations

Abstract

A method for reconstructing surfaces and analyzing surface and volume representations of the shape of an object or structure corresponding to image data, in which the structure has been modeled as one or more physically distinct compartments. The characteristics of a compartmental model are specified in terms of the material types contained in each distinct compartment and in terms of the nature of compartmental boundaries as defined by the image data. An image model that includes scalar or vector image intensity functions for each material type and for each boundary type defined by the image data is specified. Gradient functions that characterize each boundary type and some compartmental regions are specified. A set of probabilistic volume representations of the location of different compartments and the location and orientation of compartmental boundaries is generated. A combination of these probabilistic representations is used to generate a segmented volume and explicit surface reconstructions representing the shape of the structure of interest. If the structure of interest has an open rather than closed topology and if it adjoins additional structures along its natural perimeter, a method is specified for identifying the adjoining structures using a combination of logical operations and shape-changing operations applied to appropriately selected intermediate segmented volumes.