Sampling theory and automated simulations for vertical sections, applied to human brain

L. M. Cruz-Orive*, J. Gelsvartas, N. Roberts

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In recent years, there have been substantial developments in both magnetic resonance imaging techniques and automatic image analysis software. The purpose of this paper is to develop stereological image sampling theory (i.e. unbiased sampling rules) that can be used by image analysts for estimating geometric quantities such as surface area and volume, and to illustrate its implementation. The methods will ideally be applied automatically on segmented, properly sampled 2D images - although convenient manual application is always an option - and they are of wide applicability in many disciplines. In particular, the vertical sections design to estimate surface area is described in detail and applied to estimate the area of the pial surface and of the boundary between cortex and underlying white matter (i.e. subcortical surface area). For completeness, cortical volume and mean cortical thickness are also estimated. The aforementioned surfaces were triangulated in 3D with the aid of FreeSurfer software, which provided accurate surface area measures that served as gold standards. Furthermore, a software was developed to produce digitized trace curves of the triangulated target surfaces automatically from virtual sections. From such traces, a new method (called the lambda method') is presented to estimate surface area automatically. In addition, with the new software, intersections could be counted automatically between the relevant surface traces and a cycloid test grid for the classical design. This capability, together with the aforementioned gold standard, enabled us to thoroughly check the performance and the variability of the different estimators by Monte Carlo simulations for studying the human brain. In particular, new methods are offered to split the total error variance into the orientations, sectioning and cycloid components. The latter prediction was hitherto unavailable - one is proposed here and checked by way of simulations on a given set of digitized vertical sections with automatically superimposed cycloid grids of three different sizes. Concrete and detailed recommendations are given to implement the methods.

Original languageEnglish
Pages (from-to)119-150
Number of pages32
JournalJournal of Microscopy
Issue number2
Publication statusPublished - Feb 2014


  • Brain cortical thickness
  • brain cortical volume
  • Cavalieri sections
  • cycloid grid
  • human brain
  • multistage systematic sampling
  • pial surface area
  • stereology
  • subcortical surface area
  • variance components
  • variance prediction
  • vertical sections
  • MRI


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