Why Stereology?

Like a Gallop poll, stereology relies on finding the truth by avoiding all known sources of bias. The Ford Motor Company might use a Gallop poll to reveal the attitudes of U.S. car owners about Ford cars. The survey might ask car owners such questions as, “How do you rate Fords against similar cars from other manufacturers?” By surveying a representative random sample of U.S. car owners, the result would reflect not only the attitudes of the surveyed car owners but also all car owners in the U.S.. An alternative strategy would be to poll car owners outside a Ford production plant with questions like, “Fords are better cars than Chevrolets, don’t you think?” Though this approach would likely favor Ford cars, the results would offer a biased viewpoint about the attitude of all U.S. car owners. Similarly, unbiased stereology methods are specifically designed, hence “design-based,” to avoid unverifiable assumptions, false models, and other factors that could skew results from their true values.


With long-term funding from the NIH, including Small Business Innovative Research (SBIR) grants and contracts, the SRC leads the industry with more innovative features to the field of computerized stereology than any other company.

System-level Innovations

  1. Compatibility with the operating systems of PC (Windows) and Apple (iOS) computers.
  2. Three years of no-cost support (email, phone, and remote desktop access).
  3. Automatic precision calculation to ensure data collection with maximal efficiency.
  4. Dedicated support by professionally trained stereology experts.
  5. All workstations include integrated image analysis/image processing packages.
  6. All innovations are peer-reviewed by professional stereologists prior to release (see below).

Probe-level Innovations (for complete citations, see publications)

  1. Unbiased probes automatically optimized for maximal precision for all first- and second-order stereology parameters (Number, Length, Surface Area, Volume, Point-Sampled Intercepts, Spatial Distribution) for tissue sections and images sliced at any orientation, i.e., coronal, sagittal (Mouton, 2011a,b).
  2. Introduced the efficient “Space Balls” method for unbiased length and length density on arbitrary sections (Mouton et al., 2002).
  3. Introduced the Rare Event Protocol for unbiased analysis of sparse numbers of cells, i.e., rare events (Mouton, 2011a).
  4. Introduced Virtual Cycloids for unbiased estimation of surface area on arbitrary sections (Gokhale et al., 2004).
  5. Introduced Verified Computerized Stereoanalysis for automatic assessment of malignancy potential (Mouton et al., 2005).
  6. Introduced fully automatic section thickness measurement for stereology (Elozory et al., 2012).
  7. Introduced ensembles of segmentation for fully automatic analysis of malignancy potential in cancer biopsies (Chaudhury et al., 2013). 


Learn more from the Stereologer Introduction Video.