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Quantification of the 3D Morphology of the Bone Cell Network From Synchrotron Micro-Ct Images
Uppsala University, Science for Life Laboratory, SciLifeLab.
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2014 (English)In: Image Analysis and Stereology, ISSN 1580-3139, E-ISSN 1854-5165, Vol. 33, no 2, 157-166 p.Article in journal (Refereed) Published
Abstract [en]

In the context of bone diseases research, recent works have highlighted the crucial role of the osteocyte system. This system, hosted in the lacuno-canalicular network (LCN), plays a key role in the bone remodeling process. However, few data are available on the LCN due to the limitations of current microscopy techniques, and have mainly only been obtained from 2D histology sections. Here we present, for the first time, an automatic method to quantify the LCN in 3D from synchrotron radiation micro-tomography images. After segmentation of the LCN, two binary images are generated, one of lacunae (hosting the cell body) and one of canaliculi (small channels linking the lacunae). The binary image of lacunae is labeled, and for each object, lacunar descriptors are extracted after calculating the second order moments and the intrinsic volumes. Furthermore, we propose a specific method to quantify the ramification of canaliculi around each lacuna. To this aim, a signature of the numbers of canaliculi at different distances from the lacunar surface is estimated through the calculation of topological parameters. The proposed method was applied to the 3D SR micro-CT image of a human femoral mid-diaphysis bone sample. Statistical results are reported on 399 lacunae and their surrounding canaliculi.

Place, publisher, year, edition, pages
2014. Vol. 33, no 2, 157-166 p.
Keyword [en]
3D image analysis, cortical bone, morphology of lacuno-canalicular network, ramification of canaliculi, synchrotron micro-CT
National Category
Medical Image Processing
URN: urn:nbn:se:uu:diva-229539DOI: 10.5566/ias.v33.p157-166ISI: 000338449800008OAI: oai:DiVA.org:uu-229539DiVA: diva2:737093
Available from: 2014-08-11 Created: 2014-08-11 Last updated: 2014-08-11Bibliographically approved

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Image Analysis and Stereology
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