Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets

Statistical shape analysis techniques have shown to be efficient tools to build population specific models of anatomical variability. Their use is commonplace as prior models for segmentation, in which case the instance from the shape model that best fits the image data is sought. In certain cases, however, it is not just the most likely instance that must be searched, but rather the whole set of shape instances that meet certain criterion. In this paper we develop a method for the assessment of specific anatomical/morphological criteria across the shape variability found in a population. The method is based on a level set segmentation approach, and used on the parametric space of the statistical shape model of the target population, solved via a multi-level narrow-band approach for computational efficiency. Based on this technique, we develop a framework for evidence-based orthopaedic implant design. To date, implants are commonly designed and validated by evaluating implant bone fitting on a limited set of cadaver bones, which not necessarily span the whole variability in the population. Based on our framework, we can virtually fit a proposed implant design to samples drawn from the statistical model, and assess which range of the population is suitable for the implant. The method highlights which patterns of bone variability are more important for implant fitting, allowing and easing implant design improvements, as to fit a maximum of the target population. Results are presented for the optimisation of implant design of proximal human tibia, used for internal fracture fixation.