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.

Abstract. Software language descriptions comprise several heterogeneous interdependent artifacts that cover different aspects of languages (abstract syntax, notation and semantics). The dependencies between those artifacts demand the simultaneous adaptation of all artifacts when the language is changed. Changes to a language that do not change semantics are referred to as refactorings. This class of changes can be handled automatically by applying predefined types of refactorings. Refactorings are therefore considered a valuable tool for evolving a language. We present a model transformation based approach for the refactoring of software language descriptions. We use asymmetric bidirectional model transformations to synchronize the various artifacts of language descriptions with a refactoring model that contains all elements that are changed in a particular refactoring. This allows for automatic, type-safe refactorings that also includes the language tooling. We apply this approach to an Ecore, Xtext, Xtend based language description and describe the implementation of a non-trivial refactoring.

Surface-based interpolation and registration, radiation treatment, and three-dimensional visualization of two-dimensional sliced data from CT or MRT require a precise reconstruction of three-dimensional organ surfaces from two-dimensional segmentation results. Current surface-reconstruction algorithms are based on surface triangulations using heuristics to correlate and connect adjacent object slices. The approaches described in the literature can be divided into triangulations using optimization procedures, Delauny triangulations, and topology-based correlations. All approaches assume a global and invariant vertically oriented correlation strategy that can be applied equally to every organ and every slice. Surface and correlation characteristics vary greatly among bony structures and organs such as the eyes and the brain. An adjusted reconstruction of each organ according to its individual tissue characteristics is necessary to avoid errors in following processing steps such as interpolation, registration, and radiation treatment. To this end, we have designed a model-based surface-reconstruction algorithm that takes individual surface characteristics into account and allows the integration of anatomical knowledge. Three-dimensional surface models are generated from sliced data or any other source of anatomical knowledge. These models are later adjusted to the segmentations, compensating for artifacts and incomplete data.

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