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A New Straightforward Method for Automated Segmentation of Trabecular Bone from Cortical Bone in Diverse and Challenging Morphologies

View ORCID ProfileEva C. Herbst, View ORCID ProfileAlessandro A. Felder, Lucinda A. E. Evans, View ORCID ProfileSara Ajami, View ORCID ProfileBehzad Javaheri, View ORCID ProfileAndrew A. Pitsillides
doi: https://doi.org/10.1101/2021.03.02.433409
Eva C. Herbst
1Palaeontological Institute and Museum, University of Switzerland, Zurich, Switzerland;
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  • For correspondence: eva.herbst@pim.uzh.ch
Alessandro A. Felder
2Research Software Development Group, Research IT Services, University College London, London, UK;
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Lucinda A. E. Evans
3Skeletal Biology Group, Comparative Biomedical Sciences, Royal Veterinary College, London, UK;
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Sara Ajami
4UCL Great Ormond Street Institute of Child Health, London, UK;
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Behzad Javaheri
5School of Mathematics, Computer Science and Engineering, City University of London, London, UK;
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Andrew A. Pitsillides
6Skeletal Biology Group, Comparative Biomedical Sciences, Royal Veterinary College, London, UK
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/evaherbst/Trabecular_Segmentation_Avizo

  • https://figshare.com/projects/Trabecular_and_Cortical_Bone_Segmentation_Method/99434

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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A New Straightforward Method for Automated Segmentation of Trabecular Bone from Cortical Bone in Diverse and Challenging Morphologies
Eva C. Herbst, Alessandro A. Felder, Lucinda A. E. Evans, Sara Ajami, Behzad Javaheri, Andrew A. Pitsillides
bioRxiv 2021.03.02.433409; doi: https://doi.org/10.1101/2021.03.02.433409
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A New Straightforward Method for Automated Segmentation of Trabecular Bone from Cortical Bone in Diverse and Challenging Morphologies
Eva C. Herbst, Alessandro A. Felder, Lucinda A. E. Evans, Sara Ajami, Behzad Javaheri, Andrew A. Pitsillides
bioRxiv 2021.03.02.433409; doi: https://doi.org/10.1101/2021.03.02.433409

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