Mean-shifted surface curvature algorithm for automatic bone shape segmentation in orthopedic surgery planning: a sensitivity analysis

Abstract

The results of recent studies concerning statistical bone atlases and automated shape analysis are promising with a view to widening the use of surface models in orthopedic clinical practice, both in pre-operative planning and in the intra-operative stages. In this domain, automatic shape analysis is strongly advocated because it offers the opportunity to detect morphological and clinical landmarks with superior repeatability in comparison to human operators. Surface curvatures have been proposed extensively for segmentation and labeling of image and surface regions based on their appearance and shape. The surface curvature is an invariant that can be exploited for reliable detection of geometric features. In this paper, we investigate the potentiality of the algorithm termed mean-shift (MS), as applied to a non-linear combination of the minimum and maximum curvatures of a surface. We exploited a sensitivity analysis of the algorithm parameters across increasing surface resolutions. Results obtained with femur and pelvic bone surface data, reconstructed from cadaveric CT scans, demonstrated that the information content derived by the MS non-linear curvature overcomes both the mean and the Gaussian curvatures and the original non-linear curvature. By applying a threshold-based clustering algorithm to the curvature distribution, we found that the number of clusters yielded by the MS non-linear curvature is significantly lower (by a factor of up to 6) than that obtained by using the original non-linear curvature. In conclusion, this study provides valuable insights into the use of surface curvature for automatic shape analysis.

Automating the design of resection guides specific to patient anatomy in knee replacement surgery by enhanced 3D curvature and surface modeling of distal femur shape models

“…Comput Med Imaging Graph each data point using nearby points (see Appendix A.1). Considering the curvature case [33], the processing is driven by:…”

“…As an outcome, classical shape curvature descriptors, as mean and Gaussian curvatures, were shown to be insufficient for automatic 3D bone segmentation [32]. In [32,33], we proved that curvaturebased algorithms can be used to improve surface segmentation and registration by exploiting the principle of mean-shifting shape curvature. Basically, this approach involves the curvature smoothing, reducing the effect of noise and increasing the separation between concavities and convexities without modifying the surface topology.…”

Patient-specific modeling of the trochlear morphologic anomalies by means of hyperbolic paraboloids

“…The procedure to extract the TS from 3D models of the femur stems from an earlier technique in the literature, [25][26][27] which exploits the bony surface curvature to find reliable anatomical region boundaries. Such a technique was demonstrated able to automatically cut apart the trochlear groove from the distal femur surface.…”

“…For each surface model, the PCP was automatically computed by a custom optimization procedure, which maximizes the similarity between the cross-sections of the two condyles given by the PCP. [27] DPTG was computed as the most interior point on the cross-section curve through MALP and MAMP, obtained by clipping the distal femur surface through the axial plane (CT scan plane) passing through MALP.…”

“…The curve was processed to detect the posterior intercondylar notch (PIN), featuring the minimum (negative) (mean-shifted) curvature. [27] The distal intercondylar notch (DIN) was then chosen as the first point, along the curve, having positive curvature when traversing the curve from posterior to anterior direction starting from PIN. The TS was then obtained by clipping the overall distal femur surface with three planes, namely the axial plane (CT scan plane) passing through the MALP, the plane orthogonal to the axial plane containing the line DIN-MALP and the plane orthogonal to the axial plane containing the line DIN-MAMP (Figure 2(b)).…”