Clinical Contrast-enhanced Computed Tomography with Semiautomatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint.

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis. Currently, the joint geometries utilized in modelling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semiautomatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical CT with contrast enhancement and then segmented with semiautomatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic properties were generated and the mechanical responses of articular cartilage were computed during conditions of physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semiautomatic and manual segmentations were 0.05). This semiautomatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semiautomatic segmentations. Thus, the presented CT imaging based segmentation method represents a novel tool for application in FE modelling in the clinic when a physician needs to evaluate knee joint function.