Fast robust automated brain extraction

By Stephen M. Smith. 2002

11,063 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “The preprocessing protocols included a) skull removal using the BET algorithm (Smith, 2002) b) n3 bias correction using N3(Sled et al., 1998) c) segmentation using adaptive k-means (Pham and Prince, 1999) d) registration to a common template and generation of RAVENS maps using the (Davatzikos et…”
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “…the classification accuracy (for measuring the proportion of subjects correctly classified among the whole population), as well as the sensitivity (i.e., the proportion of AD or MCI patients correctly classified) and the specificity (i.e., the proportion of healthy controls correctly classified).”
Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol (2015, methodology): “Images were anonymized and the 3 Tesla T1-weighted scan was skull-stripped using BET2 software (Smith, 2002) to remove identifiable features.”
Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging (2005, methodology): “The brain was extracted from the intracranial cavity with FSL Brain Extraction Tool (BET) (Smith, 2002) applied to the early echo FSE images and segmented into three compartments (gray matter, white matter, and CSF) with FSL mFAST (Smith, 2002; Zhang et al., 2001) applied to three channels (SPGR,…”
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease (2013, methodology): “We performed brain extraction (Smith, 2002) on the T1w images associated with the 30 atlases (Hammers et al., 2003; Gousias et al., 2008), checked the brain extractions visually, and adjusted extraction parameters if needed.”
SynthStrip: skull-stripping for any brain image (2022, positive): “The widely-used Brain Extraction Tool (BET; Smith, 2002), distributed as part of the FSL package (Jenkinson et al., 2012), utilizes this technique by initializing a spherical mesh at the barycenter of the brain and projecting mesh vertices out-wards to model the brain border.”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “We have developed a tool for fully automated brain extraction which runs robustly on a variety of MR modalities (tested on T1-weighted, T2-weighted, proton density, EPI, etc.), called BET (Brain Extraction Tool) [30].”
Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, neutral): “…included motion correction (MCFLIRT tool, 6 degrees of freedom) (Jenkinson et al., 2012), slice-timing adjustment (set to the middle of each TR, via linear interpolation), brain segmentation (BET tool) (Smith, 2002), Gaussian spatial smoothing (FWHM¼ 4mm) and temporal de-trending (100sec cut-off).”
volBrain: An Online MRI Brain Volumetry System (2015, methodology): “The BET (Brain Extraction Tool) software from the FSL image processing library (Smith, 2002) is one a well-known and widely used brain extraction techniques.”
Somatotopic representation of action words in human motor and premotor cortex. (2004, neutral): “a surface model approach (“skull-stripping”) (Smith, 2002).”
Fast and robust multi-atlas segmentation of brain magnetic resonance images (2010, background): “Current state-ofthe-art brain segmentation algorithms can be classified into algorithms that label voxels (a) into brain/non-brain (Ségonne et al., 2004; Smith, 2002); (b) into different tissue types such as white ). btained from the Alzheimer’s .loni.ucla.edu\ADNI).”
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data (2006, neutral): “It is clear that the 3 skeletons are very similar, suggesting that the final skeleton is not sensitive to the set of subjects used in the target space identification or the exact target then selected.”
Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease (2004, methodology): “Loss of synaptic neurotrophic support might lead cortical areas in the brain to exhibit atrophy earlier than would have been anticipated neuropathologically (Braak and Braak, 1991; Smith, 2002a).”
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements (2014, methodology): “…work (Avants et al., 2010a) we compared an earlier version of our extraction method with publicly available brain extraction algorithms, including AFNI's 3dIntracranial (Ward, 1999), FSL's BET2 (Smith, 2002), FreeSurfer's mri_watershed (Ségonne et al., 2004), and BrainSuite (Dogdas et al., 2005).”