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Quantitative serial sectioning analysis: preview
Abstract
SUMMARY: Quantitative serial sectioning analysis provides all of the geometric information that is available from ordinary stereological analysis. In addition, it circumvents the weaknesses inherent in geometric models that are used to estimate distribution functions and topological properties. It further makes accessible some geometric aspects of microstructures that are not available stereologically, even with model assumptions. At the present state of the art it requires a prodigious effort to prepare and analyse a single sample from a single specimen. However, the current generation of image analysing computers already has the capability for automating data collection and analysis, and there is reasonable potential for automating the sample preparation procedure. Quantitative serial sectioning analysis would render obsolete all of the elegant geometrical probability arguments of stereology. Is it the wave of the future?
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Cited by
Dempster, D. W., Compston, J. E., Drezner, M. K., Glorieux, F. H., Kanis, J. A., Malluche, H., Meunier, P. J., Ott, S. M., Recker, R. R., & Parfitt, A. M. (2013) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.1805 · Cited by 2,543
Metadata
- Authors
- DeHoff, R. T.
- Venue
- Journal of Microscopy
- Publisher
- Wiley
- Published
- 1983-09
- Volume
- 131
- Issue
- 3
- Pages
- 259-263
- ISSN
- 0022-2720 (ISSN-L)
- 1365-2818
- OpenAlex
- W2160300277
- Cited by
- 172
- References
- 0
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Quantitative serial sectioning analysis: preview. https://app.cochat.ai/papers/10.1111/j.1365-2818.1983.tb04254.x
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- DeHoff, R. T. (1983). Quantitative serial sectioning analysis: preview. Journal of Microscopy. https://doi.org/10.1111/j.1365-2818.1983.tb04254.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
