In vivo hippocampal measurement and memory: a comparison of manual tracing and automated segmentation in a large community-based sample

Cherbuin, Nicolas; Anstey, Kaarin J.; Réglade-Meslin, Chantal; Sachdev, Perminder S.

doi:10.1371/journal.pone.0005265

Cited by 116Medical Image Segmentation TechniquesDementia and Cognitive Impairment ResearchMemory and Neural Mechanisms

In vivo hippocampal measurement and memory: a comparison of manual tracing and automated segmentation in a large community-based sample

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Cherbuin, Nicolas, Anstey, Kaarin J., Réglade-Meslin, Chantal, Sachdev, Perminder S.·2009·PLoS ONE

DOI 10.1371/journal.pone.0005265PMC PMC2667216

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Abstract

While manual tracing is the method of choice in measuring hippocampal volume, its time intensive nature and proneness to human error make automated methods attractive, especially when applied to large samples. Few studies have systematically compared the performance of the two techniques. In this study, we measured hippocampal volumes in a large (N = 403) population-based sample of individuals aged 44-48 years using manual tracing by a trained researcher and automated procedure using Freesurfer (http://surfer.nmr.mgh.harvard.edu) imaging suite. Results showed that absolute hippocampal volumes assessed with these methods were significantly different, with automated measures using the Freesurfer software suite being significantly larger, by 23% for the left and 29% for the right hippocampus. The correlation between the two methods varied from 0.61 to 0.80, with lower correlations for hippocampi with visible abnormalities. Inspection of 2D and 3D models suggested that this difference was largely due to greater inclusion of boundary voxels by the automated method and variations in subiculum/entorhinal segmentation. The correlation between left and right hippocampal volumes was very similar by the two methods. The relationship of hippocampal volumes to selected sociodemographic and cognitive variables was not affected by the measurement method, with each measure showing an association with memory performance and suggesting that both were equally valid for this purpose. This study supports the use of automated measures, based on Freesurfer in this instance, as being sufficiently reliable and valid particularly in the context of larger sample sizes when the research question does not rely on 'true' hippocampal volumes.

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Various studies have reported high rates of failures resulting in exclusion of large number of scans, inconsistencies in segmentations and age related bias for Freesurfer.
“Although Freesurfer is extensively validated and it has been used in a large number of studies, various studies have also reported high rates of failures resulting in exclusion of large number of scans, inconsistencies in segmentations and age related bias (Wenger et al., 2014; Cherbuin et al., 2009).”
From A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies (2020) · cites this paper as Cherbuin et al., 2009
Methods (Image analysis; page unavailable) reports that 27 scans could not be processed by the standard FreeSurfer pipeline, and Results (page unavailable) reports 13 hippocampal pairs excluded for major segmentation defects. The Discussion (page unavailable) describes differences between FreeSurfer and manual segmentations, but calls the number excluded for major defects small and says the study’s narrow age range limited its ability to detect an age-dependent effect. It therefore does not establish a high failure rate or age-related bias. (LLM, conf=0.88)
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Authors: Cherbuin, N., Anstey, K. J., Réglade-Meslin, C., & Sachdev, P. S.
Affiliations: Australian National University (AU)
UNSW Sydney (AU)
Corresponding author: Cherbuin, Nicolas
ORCID: Cherbuin, Nicolas
Anstey, Kaarin J.
Sachdev, Perminder S.
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2009-04-16
Volume: 4
Issue: 4
Pages: e5265
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0005265
PMC: PMC2667216
OpenAlex: W2061639197
Cited by: 116
References: 39
Citations by year: 2026: 1 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 6 · 2021: 4 · 2020: 5 · 2019: 6 · 2018: 8 · 2017: 6 · 2016: 10 · 2015: 13 · 2014: 9 · 2013: 11 · 2012: 7
FWCI: 10.30
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
ANU Open Research (Australian National University) (submittedVersion, cc-by)
Funders: Australian Rotary Health
Medical Research Council
National Health and Medical Research Council (973302, 366756, 418020, 157125, 179805)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords: hippocampal volume, FreeSurfer, measurement method comparison, memory performance, community sample
MeSH terms: Adult, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Memory, Middle Aged, Organ Size, Pattern Recognition, Automated, Regression Analysis, Socioeconomic Factors, Imaging, Three-Dimensional

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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In vivo hippocampal measurement and memory: a comparison of manual tracing and automated segmentation in a large community-based sample

Abstract

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