Computational neuroanatomy is an exciting new area where digital tools are used with great advantage in the analysis of structure and function of the brain. One major area of research in this field involves automatically creating computerized representations of the brain which are in a form suitable for neuroanatomic analysis. The principle drawback of contemporary methods of generating these digital models is the incompleteness and ambiguity of the input data, which is typically under-sampled relative to the features of interest. A method is presented here for creating surface-based models of neuroanatomy that address the data incompleteness issue with an integrated combination of two model-based approaches. The first approach involves applying proximity and self-intersection restrictions on surfaces in order to create a plausible neuroanatomical model in the face of data with topologies inconsistent with medical knowledge. The second approach involves identifying multiple surfaces simultaneously, with inter-surface constraints, in order to use general neuroanatomical information to correct areas where the data is incomplete or ambiguous. The overall method is one of deforming a set of polyhedral meshes, with the above constraints and others incorporated into an objective function, which is minimized to find the best fit of a model to the data. Validation of this method on simple phantoms as well as in the task of segmentation of human cortical surfaces is demonstrated. Discussion of limitations and future work is presented.
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