Empirical Modelling Workflow for Resolution Invariant Assessment of Osteophytes

Traditional quantitative analysis of bone microstructure in micro-computed tomography (microCT) is dependent on animal scale and requires parametric tuning in new implementations. This study aims to develop an automated and resolution-invariant 3D image processing workflow for quantitative assessment of osteophytes.

In this workflow, cortical bone was segmented from microCT scans, and a 3D sphere-fitting transform was performed to obtain a thickness map, for which each voxel is assigned a thickness value corresponding to the size of the largest sphere containing the voxel that fits entirely within the cortical bone. From the thickness map, a 1-voxel thick outer surface was extracted to model surface roughness. The thickness values of the outer surface were empirically estimated by a series of known statistical distributions. Resulting parameters describing best-fit distributions, along with other cortical bone metrics, were analysed to determine sensitivity to osteoarthritis and the presence of osteophytes.

The workflow was validated using microCT scans and histological gradings of rabbit and rat tibiofemoral joints. Visual inspection shows that samples with osteoarthritis and the presence of osteophytes have more surface voxels assigned small thickness values. The distribution of surface thickness values for each animal is best described by Gamma distributions, whose shape parameter is consistently sensitive to osteoarthritis and the presence of osteophytes.

Combining traditional image processing with empirical distribution fitting provides an automated, objective, and resolution-invariant workflow for osteophyte assessment.

The proposed method is simple, yet elegant in its implementation, and can be readily used in new implementations.