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Updated: Feb 18, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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A validation dataset for Macaque brain MRI segmentation.

Yaël Balbastre1,2,3, Denis Rivière3,4, Nicolas Souedet1,2

  • 1UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France.

Data in Brief
|November 24, 2017
PubMed
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This summary is machine-generated.

A new dataset of macaque brain MRIs and segmentations is now available. This resource aims to advance validation and comparison of preclinical neuroimaging analysis algorithms.

Area of Science:

  • Neuroimaging
  • Comparative Neuroanatomy
  • Medical Image Analysis

Background:

  • Validation data for preclinical image segmentation algorithms is scarce compared to human brain data.
  • Objective comparison of segmentation algorithms and identification of promising research avenues are hindered by this data gap.

Purpose of the Study:

  • To address the lack of validation data for preclinical neuroimaging.
  • To provide a reference dataset for the non-human primate imaging community.

Main Methods:

  • Acquisition of 10 T2-weighted Magnetic Resonance Images (MRIs) from healthy adult macaque brains using a 7 Tesla magnet.
  • Manual segmentation of the MRIs into 17 brain anatomical regions across 5 hierarchical levels, based on a published macaque atlas.
  • Dataset made publicly available through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).

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Main Results:

  • A unique dataset comprising 10 macaque brain MRIs with detailed manual segmentations.
  • The dataset is structured according to a previously established macaque brain atlas.
  • This dataset was utilized in the development of the 'Primatologist' primate brain morphology analysis pipeline.

Conclusions:

  • The presented dataset represents a significant step towards providing essential validation resources for non-human primate neuroimaging.
  • Availability of this data will facilitate the objective evaluation and advancement of segmentation algorithms in preclinical research.
  • This resource is expected to become a valuable reference for researchers in the non-human primate imaging field.