10Statistical shape analysis techniques have shown to be efficient tools to build pop-11 ulation specific models of anatomical variability. Their use is commonplace as prior 12 models for segmentation, in which case the instance from the shape model that 13 best fits the image data is sought. In certain cases, however, it is not just the most 14 likely instance that must be searched, but rather the whole set of shape instances 15 that meet certain criterion. In this paper we develop a method for the assessment

Development of novel implants in orthopaedic trauma surgery is based on limited datasets of cadaver trials or artificial bone models. A method has been developed whereby implants can be constructed in an evidence based method founded on a large anatomic database consisting of more than 2.000 datasets of bones extracted from CT scans. The aim of this study was the development and clinical application of an anatomically pre-contoured plate for the treatment of distal fibular fractures based on the anatomical database.48 Caucasian and Asian bone models (left and right) from the database were used for the preliminary optimization process and validation of the fibula plate. The implant was constructed to fit bilaterally in a lateral position of the fibula. Then a biomechanical comparison of the designed implant to the current gold standard in the treatment of distal fibular fractures (locking 1/3 tubular plate) was conducted. Finally, a clinical surveillance study to evaluate the grade of implant fit achieved was performed. The results showed that with a virtual anatomic database it was possible to design a fibula plate with an optimized fit for a large proportion of the population. Biomechanical testing showed the novel fibula plate to be superior to 1/3 tubular plates in 4-point bending tests. The clinical application showed a very high degree of primary implant fit. Only in a small minority of cases further intra-operative implant bending was necessary. Therefore, the goal to develop an implant for the treatment of distal fibular fractures based on the evidence of a large anatomical database could be attained. Biomechanical testing showed good results regarding the stability and the clinical application confirmed the high grade of anatomical fit.

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Extensive recent work has taken place on the construction of probabilistic atlases of anatomical organ. We propose a probabilistic atlas of ten major abdominal organs which retains structural variability by using a size-preserving affine registration, and normalizes the physical organ locations to an anatomical landmark. Restricting the degrees of freedom in the transformation, the bias from the reference data is minimized, in terms of organ shape, size and position. Additionally, we present a scheme for the study of anatomical variability within the abdomen, including the clusterization of the modes of variation. The analysis of deformation fields showed a strong correlation with anatomical landmarks and known mechanical deformations in the abdomen. The atlas and its dependencies represent a potentially important research tool for abdominal diagnosis, modeling and soft tissue interventions.

We present a framework for multi-level statistical shape analysis, applied to the study of anatomical variability of abdominal organs. Statistical models were built hierarchically, allowing the representation of different levels of detail. Principal factor analysis was used for decomposition of deformation fields obtained from non-rigid registration at different levels, and provided a compact model to study shape variability within the abdomen. To assess and ease the interpretability of the resulting deformation modes, a clustering technique of the deformation vectors was proposed. The analysis of deformation fields showed a strong correlation with anatomical landmarks and known mechanical deformations in the abdomen. Clusters of modes of deformation from fine-to-coarse levels explain tissue properties, and inter-organ relationships. Our method further presents the automated hierarchical partitioning of organs into anatomically significant components that represent potentially important constraints for abdominal diagnosis and modeling, and that may be used as a complement to multi-level statistical shape models.