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Beginning in the 1960s, scientists across a wide range of disciplines cooperated in developing unbiased – or assumption free – stereology, based on stochastic geometry and probability theory, as a way to estimate the parameters of irregularly shaped objects without introducing bias. In recent years these new estimation techniques, which were originally quite painstaking and time consuming, have made a great deal of headway in disciplines such as neuroscience, thanks in part to the development of computer software for their application. Prestigious journals and grant-giving organizations now require the use of unbiased stereology in the projects that they support, and this trend is expected to continue. Principles and Practices of Unbiased Stereology will fill a need in the biomedical community as a clear, user-friendly introduction to this area for the increasing number of scientists who need to learn these techniques for their research. The work moves logically from a discussion of the historical background of stereology to full explanations of terms, concepts,and tools, with the latter part of the manuscript devoted to typical stereology designs. An associated web site will feature color illustrations and video clips demonstrating stereological techniques.
Contents: Preface Introduction
1 The History of Stereology
4 Geometric Probes
5 Bias in Estimating Number
6 The Disector Principle
8 Length and Surface Area
9 Nonstereological Bias
10 All Variation Considered
11 Typical Stereology Designs
12 Frequent Questions about Stereology
Appendix. Conceptual Framework of Modern Stereology
Peter R. Mouton, Ph.D., is director and CEO of the Stereology Resource Center, Inc., and consulting director of the Stereology Laboratory at the Laboratory of Neurosciences Gerontology Research Center, NIA/NIH, in Baltimore.
"This is an important book for the libraries of professionals in neurobiology, neurology, and neurosurgery."
– Celso Agner, Doody's Health Sciences Review