Segmentation of cortical surface and interior brain structures using active surface / active volume templates

Advanced applications such as neurosurgical planning and simulation require both surface and interior anatomical information in order to be truly effective. We are developing a segmentation scheme based on collections of active surface templates embedded within an active volume. This composite system encodes high-level anatomical knowledge of both cortical surface and interior brain structures in a self-assembling model of a reference, or atlas brain. Following initialization of the surface templates in the test brain volume, the cortical surface templates deform to achieve a segmentation of the surface of the brain. The displacements of the cortical surface templates cause an increase in the potential elastic energy of the active volume, and a subsequent minimization of this elastic energy is used to define a volumetric warp between the reference brain and the test data. This warp is used to deform the active surface models of the deep structures in the brain to their approximate final configurations, after which a further energy minimization step achieves a final segmentation of the deep structures. The method uses the results of the surface segmentation step as a-priori information regarding the likely deformation of the deep surface models. Initial tests illustrate the potential of the system in regard to the segmentation of cortical surface and deep brain anatomy. Results will be analyzed in terms of improvements that will increase the efficacy if the system.