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Related Experiment Video

Updated: May 4, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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

L M Cruz-Orive¹, J Gelšvartas, N Roberts

¹Department of Mathematics, Statistics and Computation, Faculty of Sciences, University of Cantabria, Santander, Spain.

Journal of Microscopy

|January 16, 2014

Summary

This study introduces unbiased stereological image sampling theory for estimating geometric quantities like surface area and volume. New methods and software enable automatic estimation and error analysis in brain imaging studies.

Keywords:

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

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Last Updated: May 4, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Published on: November 14, 2019

An Unbiased Approach of Sampling TEM Sections in Neuroscience

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

Area of Science:

Neuroimaging
Stereology
Computational Anatomy

Background:

Advances in magnetic resonance imaging (MRI) and automated image analysis software are significant.
Accurate estimation of geometric quantities from medical images is crucial across disciplines.

Purpose of the Study:

To develop stereological image sampling theory for unbiased estimation of geometric quantities.
To implement and validate these methods for applications in human brain studies, particularly surface area and volume estimation.

Main Methods:

Development of unbiased sampling rules for image analysis.
Application of the vertical sections design for surface area estimation.
Introduction of a new 'lambda method' for automatic surface area estimation from digitized trace curves.
Utilizing FreeSurfer for 3D surface triangulation and Monte Carlo simulations for performance evaluation.

Main Results:

Accurate estimation of pial and subcortical surface areas, cortical volume, and mean cortical thickness.
Development of software for automatic digitization of trace curves and intersection counting.
Validation of new methods for automatic surface area estimation and error variance decomposition.

Conclusions:

The developed stereological methods provide accurate and reliable estimations of geometric quantities in brain imaging.
The new software and 'lambda method' facilitate automatic and efficient analysis.
The study offers practical recommendations for implementing these advanced image analysis techniques.