Automatic generation of dynamic 3D models for medical segmentation tasks

Models of geometry or appearance of three-dimensional objects may be used for locating and specifying object instances in 3D image data. Such models are necessary for segmentation if the object to be segmented is not separable based on image information only. They provide a-priori knowledge about the expected shape of the target structure. The success of such a segmentation task depends on the incorporated model knowledge. We present an automatic method to generate such a model for a given target structure. This knowledge is created in the form of a 3D Stable Mass-Spring Model (SMSM) and can be computed from a single sample segmentation. The model is built from different image features using a bottom-up strategy, which allows for different levels of model abstraction. We show the adequacy of the generated models in two practical medical applications: the anatomical segmentation of the left ventricle in myocardial perfusion SPECT, and the segmentation of the thyroid cartilage of the larynx in CT datasets. In both cases, the model generation was performed in a few seconds.