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Related Experiment Video

Updated: Nov 22, 2025

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Replicability, Repeatability, and Long-term Reproducibility of Cerebellar Morphometry.

Peter Sörös1,2, Louise Wölk3, Carsten Bantel4,5

  • 1Department of Neurology, Carl von Ossietzky University of Oldenburg, Heiligengeisthöfe 4, 26121, Oldenburg, Germany. peter.soros@gmail.com.

Cerebellum (London, England)
|January 9, 2021
PubMed
Summary

The CEREbellum Segmentation (CERES) tool demonstrates high accuracy and reproducibility for automated cerebellar morphometry in large-scale MRI studies, making it a reliable choice for future research.

Keywords:
CERESCerebellumFreeSurferParcellationReproducibilitySegmentation

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Area of Science:

  • Neuroimaging
  • Radiology
  • Biomedical Engineering

Background:

  • Cerebellar morphometry is crucial for understanding neurological disorders.
  • Large-scale structural MRI studies require robust and reproducible analysis methods.
  • Evaluating automated software tools is essential for consistent neuroimaging research.

Purpose of the Study:

  • To assess the replicability, repeatability, and long-term reproducibility of three automated cerebellar morphometry tools: FreeSurfer, CERES, and ACAPULCO.
  • To identify the most reliable software for future large-scale cerebellar MRI studies.
  • To provide recommendations for evaluating reproducibility in neuroimaging analyses.

Main Methods:

  • Investigated computational replicability using identical datasets and hardware.
  • Assessed repeatability by comparing scans from two sessions on the same day (Kirby-21 study).
  • Evaluated long-term reproducibility using longitudinal data (OASIS-2 study).
  • Quantified differences using percent difference, ICC, and coefficient of variation.

Main Results:

  • CERES and ACAPULCO showed variability due to stochastic algorithms; ACAPULCO had higher differences than CERES.
  • FreeSurfer and CERES demonstrated high repeatability (changes < ±5%) in short-term scans.
  • Long-term reproducibility was lower than repeatability across all tested software.
  • CERES proved to be an accurate and reproducible tool for automated cerebellar segmentation and parcellation.

Conclusions:

  • CERES is a highly accurate and reproducible tool for automated cerebellar morphometry.
  • The study provides a framework for assessing replicability, repeatability, and reproducibility in neuroimaging.
  • Recommendations are offered for selecting reliable software for large-scale cerebellar structure studies.