A new straightforward method for semi-automated segmentation of trabecular bone from cortical bone in diverse and challenging morphologies

Pitsillides, Andrew; Herbst, Eva; Felder, Alessandro; Evans, Lucinda; Anjami, Sara; Javaheri, Behzad

Abstract

Many physiological, biomechanical, evolutionary and clinical studies that explore skeletal structure and function require successful separation of trabecular from cortical compartments of a bone that has been imaged by X-ray micro-computed tomography (microCT) prior to analysis. Separation is often time-consuming, involves user bias and needs manual sub-division of these two similarly radio-opaque compartments. We have developed an objective, automated protocol which reduces user bias and enables straightforward, user-friendly segmentation of trabecular from cortical bone without requiring sophisticated programming expertise. This method can conveniently be used as a "recipe" in commercial programmes (Avizo herein) and applied to a variety of datasets. Here, we characterise and share this recipe, and demonstrate its application to a range of murine and human bone types, including normal and osteoarthritic specimens, and bones with distinct embryonic origins and spanning a range of ages. We validate the method by testing inter-user bias during the scan preparation steps and confirm utility in the architecturally challenging analysis of growing murine epiphyses. We also report details of the recipe, so that other groups can readily re-create a similar method in open access programs. Our aim is that this method will be adopted widely to create a more standardized and time efficient method of segmenting trabecular and cortical bone.

Citation

Pitsillides, A., Herbst, E., Felder, A., Evans, L., Anjami, S., & Javaheri, B. (2021). A new straightforward method for semi-automated segmentation of trabecular bone from cortical bone in diverse and challenging morphologies. Royal Society Open Science, https://doi.org/10.1098/rsos.210408

• Journal Article Type: Article
• Acceptance Date: Jul 6, 2021
• Online Publication Date: Jul 6, 2021
• Publication Date: Aug 4, 2021
• Deposit Date: Jul 8, 2021
• Publicly Available Date: Nov 11, 2021
• Journal: Royal Society Open Science
• Electronic ISSN: 2054-5703
• Publisher: The Royal Society
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1098/rsos.210408
• Public URL: https://rvc-repository.worktribe.com/output/1444021