The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Fast robust automated brain extraction
Open accessAbstract
An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre‐processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods. Hum. Brain Mapping 17:143–155, 2002. © 2002 Wiley‐Liss, Inc.
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“Thus, as in a prior pilot trial of EC (21), these individuals were at elevated risk for executive dysfunction and likely exerted more effort to successfully develop inhibitory skills, which may be reflected by increased ACC activity.”
From Evidence for Neurocognitive Plasticity in At-Risk Older Adults: The Experience Corps Program (2009) · cites this paper as [21]
Citations
Cites
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Cortical Surface-Based Analysis
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Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
Self-citationA global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018
Self-citationAccurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis
Smith, S. M., Zhang, Y., Jenkinson, M., Chen, J. M., Matthews, P. M., Federico, A., & Stefano, N. D. (2002) · NeuroImage · doi:10.1006/nimg.2002.1040 · Cited by 2,013
Self-citationNormalized Accurate Measurement of Longitudinal Brain Change
Smith, S. M., Stefano, N. D., Jenkinson, M., & Matthews, P. M. (2001) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-200105000-00022 · Cited by 489
Self-citationElastic model-based segmentation of 3-D neuroradiological data sets
Kelemen, A., Szekely, G., & Gerig, G. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.811260 · Cited by 434
Fully automatic segmentation of the brain in MRI
Atkins, M. S., & Mackiewich, B. T. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668699 · Cited by 397
Automatic detection of brain contours in MRI data sets
Brummer, M. E., Mersereau, R. M., Eisner, R. L., et al. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.232244 · Cited by 298
3-D segmentation of MR images of the head for 3-D display
Bomans, M., Höhne, Κ. H., Tiede, U., et al. (1990) · IEEE Transactions on Medical Imaging · doi:10.1109/42.56342 · Cited by 282
Surface-based labeling of cortical anatomy using a deformable atlas
Sandor, S. R., & Leahy, R. M. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.552054 · Cited by 275
Interactive 3D Segmentation of MRI and CT Volumes using Morphological Operations
Höhne, Κ. H., & Hanson, W. A. (1992) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199203000-00019 · Cited by 263
Alignment of magnetic-resonance brain datasets with the stereotactical coordinate system
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Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Self-citationThe minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Self-citationInvestigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Self-citationBakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Metadata
- Authors
- Smith, S. M.
- Affiliations
- John Radcliffe Hospital (GB)
- Corresponding author
- Smith, Stephen M.
- ORCID
- Smith, Stephen M.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2002-09-18
- Volume
- 17
- Issue
- 3
- Pages
- 143-155
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6871816
- OpenAlex
- W2071881327
- Cited by
- 10,496
- References
- 14
- Citations by year
- 2026: 338 · 2025: 439 · 2024: 517 · 2023: 527 · 2022: 611 · 2021: 669 · 2020: 689 · 2019: 688 · 2018: 657 · 2017: 647 · 2016: 620 · 2015: 678 · 2014: 564 · 2013: 564 · 2012: 505
- FWCI
- 61.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Università degli Studi di Siena
- Medical Research Council
- Engineering and Physical Sciences Research Council (GR/R77018/01)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain extraction, automated brain segmentation, deformable models
- MeSH terms
- Algorithms, Animals, Brain, Humans, Magnetic Resonance Imaging
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