Any segmentation approach assumes certain knowledge concerning data modalities, relevant organs and their imaging characteristics. These assumptions are necessary for developing criteria by which to separate the organ in question from the surrounding tissue. Typical assumptions are that the organs have homogeneous gray-value characteristics (region growing, region merging, etc.), specific gray-value patterns (classification methods), continuous edges (edge-based approaches), smooth and strong edges (snake approaches), or any combination of these. In most cases, such assumptions are invalid, at least locally. Consequently, these approaches prove to be time consuming either in their parameterization or execution. Further, the low result quality makes post- processing necessary. Our aim was to develop a segmentation approach for large 3D data sets (e.g., CT and MRI) that requires a short interaction time and that can easily be adapted to different organs and data materials. This has been achieved by exploiting available knowledge about data material and organ topology using anatomical models that have been constructed from previously segmented data sets. In the first step, the user manually specifies the general context of the data material and specifies anatomical landmarks. Then this information is used to automatically select a corresponding reference model, which is geometrically adjusted to the current data set. In the third step, a model-based snake approach is applied to determine the correct segmentation of the organ in question. Analogously, this approach can be used for model-based interpolation and registration.