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PMID: 7014911 Published · ppublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't

Measuring error and sampling variation in stereology: comparison of the efficiency of various methods for planar image analysis.

Journal of microscopy ·Vol. 121 ·No. Pt 1 ·1981-01-00 ·Pages 75-88

Mathieu O, Cruz-Orive LM, Hoppeler H, Weibel ER

Abstract

An evaluation is made of the relative efficiency (precision of the final estimate per unit time of measurement on a given set of sections) of different methods for planar analysis aimed at estimating aggregate, overall stereological parameters (such as VV, SV). The methods tested are point-counting with different densities of test points (4 less than or equal to PT less than 900 per picture), semiautomatic computer image analysis with MOP and automatic image analysis with Quantimet, for obtaining VV and SV estimates. One biological sample as well as three synthetic model structures with known coefficients of variation between sections are used. The standard error of an estimate is mainly determined by the coefficient of variation between sampling units (= sections in the present paper) so that measuring each sample unit with a very high precision is not necessary. Automatic image analysis and point-counting with a 100-point grid were the most efficient methods for reducing the relative standard errors of the VV and SV estimates to equivalent levels in the synthetic models. Using a 64-point grid was as precise, and about 11 times faster than using a tracing device for obtaining the estimate of VV in the biological sample.

MeSH Terms
Animals Computers Diaphragm Histological Techniques Microscopy/methods Microscopy, Electron/methods Mitochondria, Muscle/ultrastructure Statistics as Topic
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Mathieu O
Cruz-Orive L M
Hoppeler H
Weibel E R
Article Info
Journal
Journal of microscopy
Abbr.
J Microsc
ISSN
0022-2720
Published
1981-01-00
Pages
75-88
Language
English
Region
England
NLM ID
0204522
Subset
IM
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