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Bone morphing with statistical shape models for enhanced visualization

This paper addresses the problem of extrapolating extremely sparse three-dimensional set of digitized landmarks and bone surface points to obtain a complete surface representation. The extrapolation is done using a statistical principal component analysis (PCA) shape model similar to earlier approaches by Fleute et al. 1 This extrapolation procedure called Bone-Morphing is highly useful for intra-operative visualization of bone structures in image-free surgeries. We developed a novel morphing scheme operating directly in the PCA shape space incorporating the full set of possible variations including additional information such as patient height, weight and age. Shape information coded by digitized points is iteratively removed from the PCA model. The extrapolated surface is computed as the most probable surface in the shape space given the data. Interactivity is enhanced, as additional bone surface points can be incorporated in real-time. The expected accuracy can be visualized at any stage of the procedure. In a feasibility study, we applied the proposed scheme to the proximal femur structure. 14 CT scans were segmented and a sequence of correspondence establishing methods was employed to compute the optimal PCA model. Three anatomical landmarks, the femoral notch and the upper and the lower trochanter are digitized to register the model to the patient anatomy. Our experiments show that the overall shape information can be captured fairly accurately by a small number of control points. The added advantage is that it is fast, highly interactive and needs only a small number of points to be digitized intra-operatively.