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Cited by 122Medical Image Segmentation TechniquesAdvanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

A comparison of FreeSurfer-generated data with and without manual intervention

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McCarthy, Christopher S., Ramprashad, Avinash, Thompson, Carlie, Botti, Jo-Anna, Coman, Ioana L., Kates, Wendy R.·2015·Frontiers in Neuroscience
DOI 10.3389/fnins.2015.00379PMC PMC4612506
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Abstract

This paper examined whether FreeSurfer-generated data differed between a fully-automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation. In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. Potential exceptions to and limitations of these conclusions are discussed.

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  • Various evaluations have revealed limitations of Freesurfer that might be critical in aging and multi-site studies.

    “However, various evaluations have also revealed limitations of that approach (Mccarthy 2015; Keller et al., 2012; Mulder et al., 2014), which might be critical in aging and multi-site studies.”

    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 Mccarthy 2015

    The Introduction describes FreeSurfer segmentation errors and notes that conditions including Alzheimer’s disease may increase the need for manual editing. The Limitations section also identifies image-quality and field-strength issues. The paper does not establish that these limitations are critical specifically in aging or multi-site studies; that relevance is a plausible extension, not a finding of this evaluation. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • High number of Freesurfer exclusions based on detailed visual verification of individual ROIs was previously reported.

    “High number of Freesurfer exclusions based on detailed visual verification of individual ROIs was previously reported (Mccarthy 2015; Zandifar et al., 2017).”

    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 Mccarthy 2015

    The Image analysis methods (page unavailable) describe detailed visual inspection of FreeSurfer-generated surfaces and manual correction of errors. The paper does not report a high number of scans or ROIs excluded as a result of that inspection; its mention of excluded studies in the Introduction concerns a literature review, not FreeSurfer quality control. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
McCarthy, C. S., Ramprashad, A., Thompson, C., Botti, J.-A., Coman, I. L., & Kates, W. R.
Affiliations
SUNY Upstate Medical University (US)
Corresponding author
Kates, Wendy R.
ORCID
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2015-10-21
Volume
9
Pages
379-379
ISSN
1662-453X (ISSN-L)
DOI
10.3389/fnins.2015.00379
PMC
PMC4612506
OpenAlex
W2196212278
Cited by
122
References
105
Citations by year
2026: 2 · 2025: 9 · 2024: 6 · 2023: 16 · 2022: 14 · 2021: 21 · 2020: 18 · 2019: 9 · 2018: 15 · 2017: 10 · 2016: 2
FWCI
7.82
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (MH064824)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
FreeSurfer, manual editing, control points, white matter segmentation, cortical thickness, surface area, white matter volume, 22q11.2 deletion syndrome, intensity normalization, effect size

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