QUANTIFICATION OF THE 3D MORPHOLOGY OF THE BONE CELL NETWORK FROM SYNCHROTRON MICRO-CT IMAGES

Authors

  • Pei Dong CREATIS, CNRS UMR 5220; Inserm U1044; Université de Lyon; Université Lyon 1 ; INSA-Lyon; 69621, Villeurbanne; European Synchrotron Radiation Facility, 38043 Grenoble
  • Alexandra Pacureanu Centre for Image Analysis and Science for Life Laboratory, Uppsala, University Box, 337SE-751 05 Uppsala
  • Maria Alejandra Zuluaga
  • Cécile Olivier CREATIS, CNRS UMR 5220; Inserm U1044; Université de Lyon; Université Lyon 1 ; INSA-Lyon; 69621, Villeurbanne; European Synchrotron Radiation Facility, 38043 Grenoble
  • Quentin Grimal UMPC Univ Paris 6, UMR 7623, Laboratoire d’Imagerie Paramétrique, 75005 Paris, France
  • Françoise Peyrin CREATIS, CNRS UMR 5220; Inserm U1044; Université de Lyon; Université Lyon 1 ; INSA-Lyon; 69621, Villeurbanne; European Synchrotron Radiation Facility, 38043 Grenoble

DOI:

https://doi.org/10.5566/ias.v33.p157-166

Keywords:

3D image analysis, cortical bone, morphology of lacuno-canalicular network, ramification of canaliculi, synchrotron micro-CT

Abstract

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.

References

Anderson, E. J., Kaliyamoorthy, S., Alexander, J. I. D., and Tate, M. L. K. (2005). “Nano-Microscale Models of Periosteocytic Flow Show Differences in Stresses Imparted to Cell Body and Processes.” Annals of Biomedical Engineering, 33(1), 52–62.

Anderson, E. J., and Knothe Tate, M. L. (2008). “Idealization of pericellular fluid space geometry and dimension results in a profound underprediction of nano-microscale stresses imparted by fluid drag on osteocytes.” Journal of Biomechanics, 41(8), 1736–1746.

Beno, T., Yoon, Y.-J., Cowin, S. C., and Fritton, S. P. (2006). “Estimation of bone permeability using accurate microstructural measurements.” Journal of Biomechanics, 39(13), 2378–2387.

Bonewald, L. F. (2011). “The amazing osteocyte.” Journal of Bone and Mineral Research: The Official Journal of the American Society for Bone and Mineral Research, 26(2), 229–238.

Burger, E. H., and Klein-Nulend, J. (1999). “Mechanotransduction in bone--role of the lacuno-canalicular network.” FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 13 Suppl, S101–112.

Cardoso, L., Fritton, S. P., Gailani, G., Benalla, M., and Cowin, S. C. (2013). “Advances in assessment of bone porosity, permeability and interstitial fluid flow.” Journal of biomechanics, 46(2), 253–265.

Dierolf, M., Menzel, A., Thibault, P., Schneider, P., Kewish, C. M., Wepf, R., Bunk, O., and Pfeiffer, F. (2010). “Ptychographic X-ray computed tomography at the nanoscale.” Nature, 467(7314), 436–439.

Gu, G., Kurata, K., Chen, Z., and VÄÄNÄNEN, K. H. (2007). “Osteocyte: a Cellular Basis for Mechanotransduction in Bone.” Journal of Biomechanical Science and Engineering, 2(4), 150–165.

Han, Y., Cowin, S. C., Schaffler, M. B., and Weinbaum, S. (2004). “Mechanotransduction and strain amplification in osteocyte cell processes.” Proceedings of the National Academy of Sciences of the United States of America, 101(47), 16689–16694.

Hildebrand, T., and Rüegsegger, P. (1997). “Quantification of Bone Microarchitecture with the Structure Model Index.” Computer methods in biomechanics and biomedical engineering, 1(1), 15–23.

J.Hoshen, and R.Kopelman. (1976). “Percolation and cluster distibution. I. Cluster multiple labeling technique and critical concentration algorithm.pdf.”

Kamioka, H., Murshid, S. A., Ishihara, Y., Kajimura, N., Hasegawa, T., Ando, R., Sugawara, Y., Yamashiro, T., Takaoka, A., and Takano-Yamamoto, T. (2009). “A method for observing silver-stained osteocytes in situ in 3-microm sections using ultra-high voltage electron microscopy tomography.” Microscopy and Microanalysis: The Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada, 15(5), 377–383.

Langer, M., Pacureanu, A., Suhonen, H., Grimal, Q., Cloetens, P., and Peyrin, F. (2012). “X-Ray Phase Nanotomography Resolves the 3D Human Bone Ultrastructure.” PLoS ONE, 7(8), e35691.

Lin, Y., and Xu, S. (2011). “AFM analysis of the lacunar-canalicular network in demineralized compact bone.” Journal of microscopy, 241(3), 291–302.

Marotti, G., Ferretti, M., Remaggi, F., and Palumbo, C. (1995). “Quantitative evaluation on osteocyte canalicular density in human secondary osteons.” Bone, 16(1), 125–128.

McCreadie, B. R., Hollister, S. J., Schaffler, M. B., and Goldstein, S. a. (2004). “Osteocyte lacuna size and shape in women with and without osteoporotic fracture.” Journal of biomechanics, 37(4), 563–72.

Müller, R. (2009). “Hierarchical microimaging of bone structure and function.” Nature Reviews Rheumatology, 5(7), 373–381.

Odgaard, A. (1997). “Three-dimensional methods for quantification of cancellous bone architecture.” Bone, 20(4), 315–328.

Ohser, J., and Katja, S. (2009). 3D Images of Materials Structures: Processing and Analysis.

Ohser, J., Nagel, W., and Schladitz, K. (2009). “MILES FORMULAE FOR BOOLEAN MODELS OBSERVED ON LATTICES.” Image Analysis & Stereology, 28(2), 77–92.

Ohser, J., and Schladitz, K. (2009). 3D Images of Materials Structures: Processing and Analysis. Wiley.

Pacureanu, A., Langer, M., Boller, E., Tafforeau, P., and Peyrin, F. (2012). “Nanoscale imaging of the bone cell network with synchrotron X-ray tomography: optimization of acquisition setup.” Medical physics, 39(4), 2229–2238.

Pacureanu, A., Larrue, A., Peter, Z., and Peyrin, F. (2009). “3D non-linear enhancement of tubular microscopic bone porosities.” 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, 602–605.

Pacureanu, A., Revol-Muller, C., Rose, J., Ruiz, M. S., and Peyrin, F. (2010). “Vesselness-guided variational segmentation of cellular networks from 3D micro-CT.” 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 912 –915.

Pacureanu, A., Rollet, J., Revol-Muller, C., Buzuloiu, V., Langer, M., and Peyrin, F. (2011). “Segmentation of 3D Celluar Networks from SR-MICRO-CT Images.” 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, USA, 1970–1973.

Salomé, M., Peyrin, F., Cloetens, P., Odet, C., Laval-Jeantet, A. M., Baruchel, J., and Spanne, P. (1999). “A synchrotron radiation microtomography system for the analysis of trabecular bone samples.” Medical Physics, 26(10), 2194–2204.

Schneider, P., Meier, M., Wepf, R., and Müller, R. (2011). “Serial FIB/SEM imaging for quantitative 3D assessment of the osteocyte lacuno-canalicular network.” Bone.

Sharma, D., Ciani, C., Marin, P. A. R., Levy, J. D., Doty, S. B., and Fritton, S. P. (2012). “Alterations in the osteocyte lacunar-canalicular microenvironment due to estrogen deficiency.” Bone, 51(3), 488–497.

Sugawara, Y., Kamioka, H., Honjo, T., Tezuka, K., and Takano-Yamamoto, T. (2005). “Three-dimensional reconstruction of chick calvarial osteocytes and their cell processes using confocal microscopy.” Bone, 36(5), 877–883.

Toriwaki, J., and Yoshida, H. (2009). Fundamentals of three-dimensional digital image processing. Springer-Verlag New York Inc.

Weinbaum, S., Cowin, S. C., and Zeng, Y. (1994). “A model for the excitation of osteocytes by mechanical loading-induced bone fluid shear stresses.” Journal of Biomechanics, 27(3), 339–360.

You, L., Weinbaum, S., Cowin, S. C., and Schaffler, M. B. (2004). “Ultrastructure of the osteocyte process and its pericellular matrix.” The Anatomical Record Part A: Discoveries in Molecular, Cellular, and Evolutionary Biology, 278A(2), 505–513.

Zuluaga, M. A., Dong, P., Pacureanu, A., Orkisz, M., and Peyrin, F. (2011). “Minimum Cost Path Approach for the Segmentation of Bone Canalicular Network from Nano-CT Images.” Valencia, Spain.

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Published

2014-06-07

How to Cite

Dong, P., Pacureanu, A., Zuluaga, M. A., Olivier, C., Grimal, Q., & Peyrin, F. (2014). QUANTIFICATION OF THE 3D MORPHOLOGY OF THE BONE CELL NETWORK FROM SYNCHROTRON MICRO-CT IMAGES. Image Analysis and Stereology, 33(2), 157–166. https://doi.org/10.5566/ias.v33.p157-166

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Original Research Paper

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