Synthesizing average 3D anatomical shapes using deformable templates

A major task in diagnostic medicine is to determine whether or not an individual has a normal or abnormal anatomy by examining medical images such as MRI, CT, etc. Unfortunately, there are few quantitative measures that a physician can use to discriminate between normal and abnormal besides a couple of length, width, height, and volume measurements. In fact, there is no definition/picture of what normal anatomical structures--such as the brain-- look like let alone normal anatomical variation. The goal of this work is to synthesize average 3D anatomical shapes using deformable templates. We present a method for empirically estimating the average shape and variation of a set of 3D medical image data sets collected from a homogeneous population of topologically similar anatomies. Results are shown for synthesizing the average brain image volume from a set of six normal adults and synthesizing the average skull/head image volume from a set of five 3 - 4 month old infants with sagittal synostosis.