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The automatic segmentation of abdominal organs is a pre-requisite for many medical applications. Successful methods typically rely on prior knowledge about the to be segmented anatomy as it is for instance provided by means of active shape models (ASMs). Contrary to most previous ASM based methods, this work does not focus on individual organs. Instead, a more holistic approach that aims at exploiting inter-organ relationships to eventually segment a complex of organs is proposed. Accordingly, a flexible framework for automatic construction of multi-object ASMs is introduced, employed for coupled shape modeling, and used for co-segmentation of liver and spleen based on a new coupled shape/separate pose approach. Our first results indicate feasible segmentation accuracies, whereas pose decoupling leads to substantially better segmentation results and performs in average also slightly better than the standard single-object ASM approach.

Medical diagnoses relying on 3D data has become a procedure in clinical practice, increasing the valuable diagnostic information. However, high costs are associated with common 3D imaging modalities and therefore posing the demand for alternative approaches. Correspondingly, 2D-3D registration in terms of deforming a surface model of the anatomy of interest under the constraint of statistical plausibility has become an active field of research. In this context a quantitative analysis of the accuracy of the resulting 3D shape related to a prior established gold standard is necessary, especially since registration is based on sparse information about the patient's true anatomy. The present study discusses validation methods and results. Furthermore approaches for further improvement are provided.