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Related Experiment Video

Updated: Dec 23, 2025

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
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Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI.

Thomas Shaw1, Ashley York2, Maryam Ziaei3

  • 1Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia.

Neuroimage
|April 21, 2020
PubMed
Summary

A new pipeline, Longitudinal Automatic Segmentation of Hippocampus Subfields (LASHiS), accurately segments hippocampus subfields longitudinally. This method enhances biomarker sensitivity for diseases like Alzheimer's disease.

Keywords:
Computer-assistedHippocampusImage processingLongitudinal studiesMagnetic resonance imagingSegmentation

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Last Updated: Dec 23, 2025

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Area of Science:

  • Neuroimaging
  • Neuroanatomy
  • Biomarker Discovery

Background:

  • Longitudinal MRI analysis of hippocampus subfields offers sensitive biomarkers for neuropsychiatric disorders.
  • Subfield specialization within the hippocampus is crucial for understanding brain function and disease.

Purpose of the Study:

  • To evaluate a novel automated longitudinal segmentation pipeline for hippocampus subfields called LASHiS.
  • To compare LASHiS performance against existing segmentation methods.

Main Methods:

  • LASHiS uses joint-label fusion to propagate labels to a subject-specific template for unbiased segmentation.
  • The pipeline leverages multi-contrast MRI data at 3T and 7T.
  • It was validated using test-retest reliability and Bayesian Linear Mixed Effects models.

Main Results:

  • LASHiS demonstrated robust and reliable automatic multi-contrast segmentation of hippocampus subfields.
  • Higher volume similarity and Dice coefficients indicated strong test-retest reliability.
  • The pipeline showed sound performance at 3T and robust results at 7T.

Conclusions:

  • LASHiS provides a reliable tool for longitudinal volumetric and morphometric analysis of hippocampus subfields.
  • This method can improve the detection of subtle changes in diseases like Alzheimer's.
  • The open-source code facilitates further research in neuroimaging biomarkers.