FreeSurfer
Open accessAbstract
FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.
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The most popularly used cortical parcellation is the Freesurfer Desikan-Killiany cortical parcellation, while many other cortical parcellation schemes have also been widely used.
“The most popularly used cortical parcellation is the Freesurfer Desikan-Killiany cortical parcellation (Desikan et al., 2006; Fischl, 2012), while many other cortical parcellation schemes have also been widely used (Destrieux et al., 2010; Shattuck et al., 2008; Tzourio-Mazoyer et al., 2002).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Fischl, 2012
The Introduction (page not provided) identifies the Desikan et al. (2006) cortical parcellation as part of FreeSurfer, and the Contributors section says Desikan and Killiany developed it. Neither passage establishes that it is the most popularly used cortical parcellation. The later clause about other schemes is cited to other sources, not Fischl (2012). (LLM, conf=0.84)
Experimental — accuracy not yet measuredAll data sets were corrected for head motion, eddy currents and susceptibility distortions and are, in general, of very high quality.
“All data sets were corrected for head motion, eddy currents and susceptibility distortions and are, in general, of very high quality69–73.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [71]
The FreeSurfer section on surface deformation and thickness estimation discusses cortical-surface modeling and how the method handles some MRI acquisition artifacts. It does not say that the citing paper’s data sets were corrected for head motion, eddy currents, or susceptibility distortions, or assess their overall quality (searched those correction terms). (LLM, conf=0.76)
Experimental — accuracy not yet measuredSurface-based registrations that take into account sulcal and gyral anatomy also help mitigate confounds from developmental changes.
“Surface based registrations that take into account the sulcal and gyral anatomy also help mitigate these confounds, and may be the future direction of the field (Fischl, 2012; Van Essen, 2012).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Fischl, 2012
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Self-citationA Genetic Algorithm for the Topology Correction of Cortical Surfaces
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Self-citationLocalization of focal epileptic discharges using functional connectivity magnetic resonance imaging
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Selective Disruption of the Cerebral Neocortex in Alzheimer's Disease
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Self-citationA method for identifying geometrically simple surfaces from three-dimensional images
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Regional cortical thickness matters in recall after months more than minutes
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Self-citationImproved tractography alignment using combined volumetric and surface registration
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- Authors
- Fischl, B.
- Affiliations
- Harvard University (US)
- Corresponding author
- Fischl, Bruce
- ORCID
- Fischl, Bruce
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-08
- Volume
- 62
- Issue
- 2
- Pages
- 774-781
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3685476
- OpenAlex
- W4241074797
- Semantic Scholar
- 771b3c73fbaba12f5d65db39401ee9cddb224554
- Cited by
- 9,526
- References
- 96
- Citations by year
- 2026: 1247 · 2025: 1431 · 2024: 1203 · 2023: 1051 · 2022: 998 · 2021: 912 · 2020: 688 · 2019: 512 · 2018: 491 · 2017: 332 · 2016: 255 · 2015: 188 · 2014: 131 · 2013: 66 · 2012: 9
- FWCI
- 17.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Center for Research Resources (P41-RR14075, BIRN002, U24 RR021382)
- National Institute on Aging (AG022381)
- National Institute of Neurological Disorders and Stroke (1S10RR023401, R01 NS052585-01, 1R01NS070963, 1S10RR023043, 1S10RR019307, 1R21NS072652-01)
- Ellison Medical Foundation
- National Institute of Biomedical Imaging and Bioengineering (R01EB006758)
- 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, neuroimaging data analysis, cortical surface reconstruction, T1-weighted imaging, brain connectivity, open source software
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software, History, 20th Century, History, 21st Century
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