An Automated Algorithm to Detect the Trabecular-Cortical Bone Interface in Micro-Computed Tomographic Images

Abstract

Micro-computed tomography (microCT) has become a standard tool for the evaluation of bone morphology in preclinical studies. Unfortunately, the user-dependent definition of contour lines that separate trabecular from cortical bone is not only extremely time-consuming but may also represent a source of data bias and increased variability. Here, an automated image segmentation technique was developed and tested over a large range of bone phenotypes. The principal steps of the algorithm involve blurring, segmentation at different thresholds, and volumetric component labeling to first identify the periosteal edge and then create a cortical mask, the inner edge of which defines the trabecular-cortical interface. The algorithm was tested against (1) eight skilled microCT operators who manually defined the trabecular bone within the distal femur of four adult mice as well as (2) contour lines drawn by a single user in femurs from 71 rodents. Across the four femurs, the coefficient of variation between users was 9% for bone volume fraction, 13% for connectivity density, and 3% for trabecular thickness. Morphometric data produced by the algorithm were within 2% of the mean values of the eight operators. Across the 71 femurs, the slope and intercept of the regressions between morphometric automatic and user data were, with the exception of trabecular thickness, not significantly different from 1 and 0, respectively. Because of the excellent match with the current gold-standard technique, this algorithm may present a valuable tool for the standardized and automated evaluation of bone morphology without the time-consuming task of drawing contour lines.