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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
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Related Experiment Video

Updated: Feb 8, 2026

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
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The EADC-ADNI harmonized protocol for hippocampal segmentation: A validation study.

Azar Zandifar1, Vladimir S Fonov2, Jens C Pruessner3

  • 1McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada.

Neuroimage
|July 4, 2018
PubMed
Summary
This summary is machine-generated.

A new harmonized protocol for hippocampal segmentation (HarP) maintains accuracy for Alzheimer's research. This method effectively captures disease-related atrophy and can be used in clinical settings.

Keywords:
Alzheimer's diseaseArea under receiver operating characteristic curve (AUC)Cohen's dDice's κEADC-ADNI harmonized protocol (the HarP)Hippocampal segmentationPruessner protocol

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Area of Science:

  • Neuroimaging
  • Medical image analysis
  • Alzheimer's disease research

Background:

  • Manual hippocampal segmentation is crucial for Alzheimer's disease (AD) research.
  • Existing protocols vary, necessitating harmonization for large-scale studies.
  • The EADC-ADNI Harmonized Protocol (HarP) was developed to standardize manual segmentation.

Purpose of the Study:

  • To compare the HarP with a widely used protocol (Pruessner, 2000) for manual hippocampal segmentation.
  • To evaluate the conformity of automatic segmentation trained on each protocol.
  • To assess the ability of each protocol to detect Alzheimer's disease-related hippocampal atrophy.

Main Methods:

  • Developed a single automatic segmentation method trained on manual segmentations from both HarP and Pruessner protocols.
  • Measured Dice's kappa coefficient to assess automatic segmentation conformity with manual labels.
  • Evaluated the ability to capture Alzheimer's disease-related hippocampal volume differences in large datasets.
  • Calculated effect sizes (Cohen's d) and ROC curves for classifiers distinguishing between AD/NC and sMCI/pMCI.

Main Results:

  • The HarP demonstrated comparable automatic segmentation conformity to the Pruessner protocol (Dice's κ=0.87 vs. 0.88).
  • HarP-derived segmentations effectively captured Alzheimer's disease-related hippocampal volume differences.
  • Significant effect sizes were observed for distinguishing AD vs. NC (Cohen's d = 1.5883) and sMCI vs. pMCI (Cohen's d = 0.5747).
  • A classifier using HarP-derived volumes achieved high accuracy for AD:NC (AUC=0.8858) and moderate accuracy for sMCI:pMCI (AUC=0.6677).

Conclusions:

  • The EADC-ADNI Harmonized Protocol (HarP) provides a reliable and harmonized approach for manual hippocampal segmentation.
  • HarP-derived labels are suitable for large-scale clinical and research applications.
  • The protocol is sensitive and accurate for delineating the hippocampus in the context of Alzheimer's disease research.