Stereology

Stereology is a set of mathematical and statistical methods used to obtain quantitative three-dimensional (3D) information from two-dimensional (2D) sections or images. It aims to produce unbiased estimates of structural features such as volume, surface area, length, and number of objects within a defined region. Central to stereology is the use of systematic random sampling to avoid biases related to section orientation, size, or tissue processing.

Core concepts include the Cavalieri principle for estimating volume from cross-sectional areas, point counting as a simple method to derive areas and volumes, and line or intercept methods that estimate length or surface from the frequency of intersections with a probe. The disector principle provides unbiased counting of objects within a known 3D sampling frame, and the optical disector applies this in microscopic tissue sections using optical sections. The optical fractionator combines disector sampling with fractionator sampling to estimate total object numbers, and is widely used for counting cells such as neurons in brain structures.

Stereology has broad applications across biology, medicine, and materials science. In neuroscience, it is used to estimate total neuron or glial cell numbers, as well as synapse or capillary densities. In pathology and histology, it supports quantitative cell counts and tissue architecture analyses. In materials science, stereology helps quantify porosity, grain size, and fiber lengths. Practical use requires meticulously prepared samples, systematic random sampling, and adherence to unbiased counting rules to minimize bias and error, while acknowledging potential distortions from tissue processing.