Evidence suggests that subchondral bone can be used as a predictor for the onset of osteoarthritis. As such, there is a need to accurately and reproducibly quantify subchondral bone in areas where osteoarthritis develops. In this paper, we present a novel technique for the segmentation of subchondral bone in the tibiofemoral joint and assess the reproducibility of this method with multiple measures and users.
The right hind leg of seven C57BL/6 mice were excised and imaged in μCT. The menisci and patella were manually segmented and the image data was Gaussian filtered and binarized. An in-house algorithm was used to generate cortical and epiphyseal volumes of interest and standard morphometric indices for bone were computed. The intraclass correlation coefficient (ICC), absolute precision error (PE(SD)), and precision error as a percentage of the coefficient of variation of the repeated measurements (PE(%CV)) were calculated for each index. Additionally, an inter-user study was performed using the same indices and statistics.
For repeated measures, ICC ranged from 0.869 (cortical bone volume fraction, femur) to 0.994 (degree of anisotropy, femur). Similarly, PE(%CV) ranged from 0.84% (cortical bone volume fraction, femur) to 5.11% (connectivity density, tibia). For repeated users, no effect was seen in the femur with a slight effect in the tibia.
A novel method for the automatic segmentation of cortical and epiphyseal bone is presented and is shown to be reproducible in C57BL/6 mice. This tool will allow for high-throughput studies of osteoarthritis in animal models.