A hybrid approach to the skull stripping problem in MRI

By Florent Ségonne, A. M. Dale, Evelina Busa, Maureen Glessner, David H. Salat, Horst K. Hahn, Bruce Fischl. NeuroImage, 2004

2,376 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1f419146ef08534c23f642e0136dcd73321ace43

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Citations in the corpus

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness (2014, neutral): “The steps of this processing are described in detail elsewhere (Ségonne et al., 2007; Ségonne et al., 2004; Fischl and Dale, 2000; Fischl et al., 1999; Dale et al., 1999; Sled et al., 1998).”
Precision functional mapping of individual human brains (2017, positive): “…including generation of white matter and pial surfaces, inflation of the surfaces to a sphere, and surface shape-based spherical registration of the subject’s original surface to the fsaverage surface (Dale and Sereno, 1993; Dale et al., 1999; Fischl et al., 1999; Ségonne et al., 2004).”
Diffusion tensor imaging shows white matter tracts between human auditory and visual cortex (2011, positive): “In brief, non-brain tissue was removed (Segonne et al. 2004), images were intensity corrected and normalized (Sled et al. 1998), subcortical volumetric structures were segmented (Fischl et al. 2002, 2004a) and the grey–white matter boundary was tesselated and topologic inaccuracies automatically…”
BEaST: Brain extraction based on nonlocal segmentation technique (2012, neutral): “…histogram analysis (Shan et al., 2002), watershed (Hahn and Peitgen, 2000), graph cuts (Sadananthan et al., 2010), label fusion (Leung et al., 2011), and hybrid techniques (Carass et al., 2011; Iglesias et al., 2011; Rehm et al., 2004; Rex et al., 2004; Segonne et al., 2004; Shattuck et al., 2001).”
FreeSurfer (2012, methodology): “, 2002); Florent Ségonne developed the hybrid watershed skull stripping algorithm still in use in FreeSurfer (Ségonne et al., 2004), and also many volumetric and surface-based algorithms for topology correction, and evolving fronts under topological control (Ségonne et al.”
Mapping Human Cortical Areas in vivo Based on Myelin Content as Revealed by T1- and T2-weighted MRI (2011, methodology): “edu/), which includes brain extraction, intensity normalization, segmentation, generation of white and pial surfaces, surface topology correction, inflation of surfaces to a sphere, and spherical registration to the fsaverage surface based on a measure of surface shape (Sled et al., 1998; Dale et al”
Unique developmental trajectories of cortical thickness and surface area (2014, positive): “…involves motion correction, removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Clarkson et al., 2011; Segonne et al., 2004), Talairach transformation (affine registration), segmentation of subcortical white matter, deep gray matter structures and…”
Decreased white matter integrity in late-myelinating fiber pathways in Alzheimer's disease supports retrogenesis (2008, methodology): “The T1 images were skull stripped with Hybrid Watershed algorithm (Gootjes et al., 2004; Segonne et al., 2004), Brain Surface Extractor (BSE, Sandor and Leahy, 1997; Shattuck and Leahy, 2001), or both, as these two programs have been shown to be more effective than others (e.”
Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure. (2010, methodology): “The cortical surface was reconstructed to measure thickness at each surface location, or vertex, using a semiautomated approach described elsewhere (Dale and Sereno 1993; Dale et al. 1999; Fischl et al. 1999a, 1999b, 2001; Fischl and Dale 2000; Salat et al. 2004; Segonne et al. 2004, 2005).”
Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths (2008, methodology): “, 1999a) and has undergone several important improvements over the years (Fischl et al., 1999b, 2001, 2002a; Segonne et al., 2004).”
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, methodology): “Moreover, FreeSurfer usually fails to remove the dura matter, which is a well-known flaw of the algorithm (see IBSR-1 in Figure 15).”
Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. (2009, methodology): “These images underwent a white matter-based segmentation, grey/white matter and pial surfaces were defined, and topology correction was applied to these reconstructed surfaces (Dale et al., 1999; Fischl et al., 2001; Segonne et al., 2004).”
A review of atlas-based segmentation for magnetic resonance brain images (2011, neutral): “[104] F.”
The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements (2012, methodology): “The volume-based stream involves correction for intensity non-uniformity in the MRI data [3], affine transformation to the MNI305 template, intensity normalisation, removal of non-brain tissue [4], linear and non-linear transformations to a probabilistic brain atlas, and finally, labeling of cortica”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “…2011; Zuo and Xing, 2011), 2) brain surface reconstruction via the recon-all command in FREESURFER (Dale et al., 1999; Fischl et al., 1999a,b, 2001; Segonne et al., 2004, 2007), which has been demonstrated to show good test–retest reliability across scanner manufacturers and across field strengths…”
Decreased segregation of brain systems across the healthy adult lifespan (2014, methodology): “This pipeline included brain extraction, segmentation, generation of white matter and pial surfaces, inflation of the surfaces to a sphere, and surface shape-based spherical registration of the participant's ‘native’ surface to the fsaverage surface (23-27).”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, background): “Furthermore, this problem is faced routinely in brain image processing labs (Ségonne et al., 2004; Sadananthan et al., 2010; Park and Lee, 2009; Lim and Pfefferbaum, 1989; de Boer et al., 2010; Acosta-Cabronero et al., 2008).”