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Biomarkers.

Jiongqi Qu1, Sophie A Martin1, Anna Schroder1

  • 1University College London, London, United Kingdom.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Automated hippocampal segmentation tools show varying clinical readiness. FastSurfer and SynthSeg excel in disease sensitivity, while FastSurfer, SynthSeg, GIF, and nnUNet offer improved test-retest reliability over FreeSurfer.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Deep learning is widely used for automated hippocampal segmentation in dementia research.
  • Current focus is often on segmentation accuracy, neglecting clinical readiness factors like disease sensitivity and reliability.
  • Evaluating these factors is crucial for translating automated tools into clinical practice.

Purpose of the Study:

  • To assess the sensitivity to Alzheimer's disease and mild cognitive impairment, and the test-retest reliability of five automated hippocampal segmentation pipelines.
  • To benchmark these pipelines against FreeSurfer, a widely used standard.
  • To establish a workflow for evaluating the clinical readiness of automated segmentation tools.

Main Methods:

  • Compared five deep learning pipelines (FastSurfer, SynthSeg, GIF, InnerEye, nnUNet) against FreeSurfer 7.4.
  • Evaluated group-level sensitivity using T1-weighted MRI scans from ADNI (n=2299) and NACC (n=1852).
  • Assessed reliability using within-scanner, between-scanner, and motion artifact datasets (1264 scans).

Main Results:

  • All pipelines demonstrated large effect sizes for Alzheimer's disease sensitivity; FastSurfer and SynthSeg showed the highest.
  • Pipeline rankings for sensitivity remained consistent across different cognitive states and scan quality.
  • FastSurfer, SynthSeg, GIF, and nnUNet exhibited substantially higher test-retest reliability than FreeSurfer on within-scanner data.

Conclusions:

  • The study provides a workflow for assessing clinical readiness of automated hippocampal segmentation tools.
  • FastSurfer and SynthSeg demonstrate strong potential for clinical application due to high sensitivity and reliability.
  • The evaluation strategy can inform the development of robust pipelines resilient to artifacts and scan variability.