Automatic segmentation editing for cortical surface reconstruction

Segmentation and representation of the human cerebral cortex from magnetic resonance images is an important goal in neuroscience and medicine. Accurate cortical segmentation requires preprocessing of the image data to separate certain subcortical structures from the cortex in order to generate a good initial white-matter/gray-matter interface. This step is typically manual or semi-automatic. In this paper, we propose an automatic procedure that is based on a careful analysis of the brain anatomy. Following a fuzzy segmentation of the brain image, the method first extracts the ventricles using a geometric deformable surface model. A region force, derived from the cerebrospinal membership function, is used to deform the surface towards the boundary of the ventricles, while a curvature force controls the smoothness of the surface and prevents it from growing into the outer pial surface. Next, region-growing identifies and fills the subcortical regions in each cortical slice using the detected ventricles as seeds and the white matter and several automatically determined sealing lines as boundaries. To make the method robust to segmentation artifacts, a putamen mask drawn in the Talairach coordinate system is also used to help the region growing process. Visual inspection and initial results on 15 subjects show the success of the proposed method.