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

Abstract

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

Morphometric analysis of anatomical implant forms for minimally invasive acetabular fracture osteosynthesis

“…Implant fit tests often rely on synthetic rapid prototype models or cadaver bones. Virtual tests address the practicability, accuracy and longterm cost-effectiveness, particularly when performed semi-or fully automatically [32,33]. Such tests should resort to large databases covering the anatomical variation of a representative population.…”

Capturing the multiscale anatomical shape variability with polyaffine transformation trees

“…Common key steps to population-based design (Kozic et al, 2010;Bou-Sleiman et al, 2011;Metzger et al, 2011), are registration to capture shape variability as encountered in a population and statistical analysis of the registration results, performed subsequently and independently. As mentioned, registration approaches for implant design should consider not only bone surface information, but also volumetric information describing the bone density distribution, which is needed to compute best location and orientation of the placement of screws to fixate implants.…”

“…In Cervantes et al (2012), flexible implants that allow the surgeon to adjust the geometry after bone fixation are presented. Instead of pre-manufacturing patient specific implants, there have been several works on population-based designs, for femurs (Kozic et al, 2010;Bou-Sleiman et al, 2011;Bonaretti et al, 2011) and mandibles (Metzger et al, 2011). In these approaches, the population is stratified into several sub-populations according to morphological differences or meta-information (e.g.…”

Polar Histograms of Curvature for Quantifying Skeletal Joint Shape and Congruence

“…An alternative method for describing joint shape is based on fitting circles or ellipsoids to the articular surfaces [18], but this method is not extendable to joints with concave and convex portions (e.g., saddle joints). Statistical shape models (SSMs) have also been used to describe whole bone shape [19][20][21] and to identify drastic changes in bony morphology that occur with severe pathology [21,22]. The feasibility of SSMs and circle-or ellipsoid-fitting methods in identifying subtle anatomical differences in articular geometry, however, has not been demonstrated.…”