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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis.

W Huizinga1, D H J Poot1, E J Vinke2,3

  • 1Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands.

Frontiers in Big Data
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

Different automated brain image segmentation methods show systematic differences, impacting disease sensitivity. While consistent for diagnostic z-scores across methods, interchangeability varies by brain region, especially for smaller structures.

Keywords:
brain region segmentationcomparison studymagnetic resonance imagingnormative modelingsubcortical

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Automated segmentation of magnetic resonance brain images is crucial for anatomical region analysis.
  • Existing methods may produce systematic differences, affecting disease sensitivity and comparison to normative data.
  • The interchangeability of different segmentation methods for normative and patient-specific analyses remains unclear.

Purpose of the Study:

  • To assess the interchangeability of five state-of-the-art brain image segmentation methods.
  • To determine if different methods can be used for computing normative data and assessing patient volumes.
  • To evaluate the impact of segmentation method choice on single-subject Alzheimer's disease analysis.

Main Methods:

  • Compared volumes of six brain regions from 988 non-demented subjects using five methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation-maximization (MALP-EM), and model-based brain segmentation (MBS).
  • Calculated partial correlation coefficient (PCC-v) and intraclass correlation coefficient (ICC-v) for volume agreement.
  • Applied methods to 42 Alzheimer's disease patients to assess diagnostic z-scores and their agreement across methods.

Main Results:

  • High PCC-v across methods indicates systematic volume differences, while lower ICC-v, especially for smaller regions, emphasizes the need for consistent method application.
  • Diagnostic z-scores for Alzheimer's disease patients were consistent across methods.
  • High absolute agreement (ICC-z) for thalamus and putamen suggests interchangeability for these regions; other regions showed variable agreement.

Conclusions:

  • While diagnostic performance is consistent, the interchangeability of brain segmentation methods varies significantly by anatomical region.
  • It is essential to use the same method for generating normative data and assessing patient volumes.
  • Confirmation of method interchangeability is recommended for specific applications and datasets, particularly for smaller brain structures.