A meta-algorithm for brain extraction in MRI
David E. Rex,aDavid W. Shattuck,aRoger P. Woods,bKatherine L. Narr,aEileen Luders,a
Kelly Rehm,cSarah E. Stolzner,bDavid A. Rottenberg,cand Arthur W. Togaa,*
aLaboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1769, USA
bDepartment of Neurology, Neuropsychiatric Institute, Ahmanson-Lovelace Brain Mapping Center, David Geffen School of Medicine at UCLA,
Los Angeles, CA 90095-1769, USA
cDepartments of Radiology and Neurology, Minneapolis VA Medical Center, University of Minnesota, Minneapolis, MN 55417, USA
Received 20 February 2004; revised 8 June 2004; accepted 9 June 2004
Available online 12 September 2004
Accurate identification of brain tissue and cerebrospinal fluid (CSF) in
a whole-head MRI is a critical first step in many neuroimaging studies.
Automating this procedure can eliminate intra- and interrater variance
and greatly increase throughput for a labor-intensive step. Many
available procedures perform differently across anatomy and under
different acquisition protocols. We developed the Brain Extraction
Meta-Algorithm (BEMA) to address these concerns. It executes many
extraction algorithms and a registration procedure in parallel to
combine the results in an intelligent fashion and obtain improved
results over any of the individual algorithms. Using an atlas space,
BEMA performs a voxelwise analysis of training data to determine the
optimal Boolean combination of extraction algorithms to produce the
most accurate result for a given voxel. This allows the provided
extractors to be used differentially across anatomy, increasing both the
accuracy and robustness of the procedure. We tested BEMA using
modified forms of BrainSuite’s Brain Surface Extractor (BSE), FSL’s
Brain Extraction Tool (BET), AFNI’s 3dIntracranial, and FreeSurfer’s
MRI Watershed as well as FSL’s FLIRT for the registration procedure.
Training was performed on T1-weighted scans of 136 subjects from five
separate data sets with different acquisition parameters on separate
scanners. Testing was performed on 135 separate subjects from the
same data sets. BEMA outperformed the individual algorithms, as well
as interrater results from a subset of the scans, when compared for the
mean Dice coefficient, a rating of the similarity of output masks to the
manually defined gold standards.
D 2004 Elsevier Inc. All rights reserved.
Keywords: Algorithm; Meta-algorithm; Brain extraction; Segmentation;
Automated processing; MRI
many studies in neuroimaging. Low-level classification of the brain
allows for the analysis of cortical structure (Fischl et al., 1999;
Thompsonetal.,2001) providesameasureofbrainvolume (Lawson
et al.,2000;Smithet al.,2002), improvesthelocalizationofsignalin
magnetoencephalography and electroencephalography data (Baillet
et al., 1999; Dale and Sereno, 1993), can initialize a more detailed
segmentation of tissues (Shattuck et al., 2001; Zhang et al., 2001),
and can be used to prepare data for accurate image registration
(Woods et al., 1993). Automating the brain identification step in
for larger sample sizes by doing away with a labor-intensive step.
Brain extraction algorithms
Numerous algorithms have been written to perform brain
extraction. Most are devised to work on T1-weighted MRI data,
with several exceptions into other modalities (Alfano et al., 1997;
Bedell and Narayana, 1998; Held et al., 1997). Various methodol-
ogies are used to achieve a semiautomated (Bomans et al., 1990;
Hohne and Hanson, 1992) or fully automated (Dale et al., 1999;
Smith, 2002) separation of brain from nonbrain tissue.
Atlas registration techniques for segmentation transfer brain
labels to an individual subject (Bajcsy et al., 1983; Christensen
et al., 1996; Collins et al., 1995; Davatzikos, 1997; Miller et al.,
but may fail at the cortical surface due to the large degree of
intersubject variability in sulcal and gyral morphology. Improved
atlas techniques joining low-level tissue classifications (gray matter,
white matter, and cerebrospinal fluid) with image registration have
had more success in demarcating anatomy (Collins et al., 1999;
Kapur et al., 1996).
Anearly semiautomated technique for brain extraction usesedge
detection to demarcate connected tissues within a slice (Bomans et
al., 1990). Components that represent brain are manually selected
to complete the process. Sandor and Leahy (1997) developed an
automated edge-detection technique using anisotropic diffusion
filtering, Marr–Hildreth edge detection, and a sequence of morpho-
logical processing steps to extract the brain in three dimensions.
Shattuck et al. (2001) subsequently improved upon this technique.
Slice by slice brain identification based on gray matter and
white matter intensity estimation, connected component determi-
1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved.
* Corresponding author. Laboratory of Neuro Imaging, Department of
Neurology, Room 4-238, 710 Westwood Plaza, Box 951769, Los Angeles,
CA 90095-1769. Fax: +1-310-206-5518.
E-mail address: firstname.lastname@example.org (A.W. Toga).
Available online on ScienceDirect (www.sciencedirect.com.)
NeuroImage 23 (2004) 625–637
nation, and morphology operations also have produced good
results (Brummer et al., 1993; Lemieux et al., 1999; Ward,
1999). Lemieux et al. (2003) further extended these techniques
to include CSF estimation for the inclusion of all intracranial CSF.
Deformable templates guided by image intensity information,
usually the search for the gray matter or CSF border, and
smoothness constraints that mimic general properties of the brain
also have been used (Dale et al., 1999; MacDonald et al., 1994;
MacDonald et al., 2000; Smith, 2002).
A meta-algorithm uses the results of individual algorithms for
similar tasks, or subtasks, to perform the chosen task. Many meta-
algorithms have been designed to achieve higher reliability or to
attain greater accuracy using a trained system. Schroder et al.
(1999) implemented a meta-algorithm for the deconvolution of
disturbed data, called Munchhausen, to calculate blood volume
using the intravascular concentration time course of an injected
substance. They note that many deconvolution techniques vary in
their performance depending on the type of data and the nature of
the disturbance in the recorded values. Munchhausen uses a data-
driven decision rule to select from its many deconvolution techni-
ques to achieve more robust results than any individual algorithm.
Shaaban and Schalkoff (1995) and Schalkoff and Shaaban
(1999) use a meta-algorithm to solve general image processing
and feature extraction problems for two-dimensional images. A
training set showing initial images and outlining the desired
features to extract is used to solve a classification problem in an
algorithm graph. Multiple algorithm paths exist through the graph
and the training guide selection of the best processing path. The
results can be applied to new data for identification of features
defined by the training set.
Meta-algorithms also have been used previously for studies on
MRI scans. Rehm et al. (1999) implemented and validated (Boesen
et al., 2003) a meta-algorithm for brain extraction from an MRI
volume called McStrip. It uses a polynomial registration (Woods et
al., 1998) to provide a brain mask from an atlas and builds a
threshold mask from estimates of tissue class boundaries. It also
generates a BSE mask from the Brain Surface Extractor (Shattuck
et al., 2001) using many parameter sets and choosing the mask in
highest agreement with the threshold mask. The union of the
threshold mask and the BSE mask provides the final output.
McStrip outperformed three other algorithms, BSE, BET (Smith,
2002), and SPM (Ashburner and Friston, 2000) in both boundary
similarity and misclassified tissue metrics for 15 test scans.
Collins et al. (1999) implemented a meta-algorithm for gross
cerebral structure segmentation. A nonlinear registration is used to
obtain tissue labels from an atlas, and a low-level tissue classifi-
cation identifies regions of gray matter, white matter, and cerebro-
spinal fluid. The reconciliation of the two segmentations produces
a more accurate identification of cerebral structures than either
method produces on its own.
Brain extraction meta-algorithm
A single algorithm often will not adequately perform the
neuroimaging task in every subject across an entire data set. Often,
many different procedures must be attempted or manual interven-
tion utilized to achieve acceptable results. An environment that
presents many similar algorithms is a simple way to access and test
various methods. A meta-algorithm that allows the specification of
a general procedure and obtains a valid result, regardless of input
data, would allow the task of deciphering the results from many
algorithms and selecting the best procedures to be fully docu-
mented and automated.
Each of the aforementioned algorithms for brain identification
possesses strengths and weaknesses that vary with scanning
protocol, image characteristics such as contrast, signal-to-noise
ratio, and resolution, and subject-specific characteristics like age
and atrophy (Fennema-Notestine et al., 2003). Algorithms may
also vary in their accuracy in different anatomic regions. The
development of a meta-algorithm that intelligently utilizes the
strengths of the contributing subalgorithms should obtain results
that are, on average, superior to any individual algorithm. We
developed and tested such a meta-algorithm using multiple extrac-
tion procedures in concert with a registration procedure. It achieves
improved results using a variety of anatomically specified Boolean
functions to combine the results of the extractors.
Brain extraction meta-algorithm
The Brain Extraction Meta-Algorithm (BEMA) uses four freely
available brain extraction algorithms and a linear volume registra-
tion procedure, which does not require skull stripping, in concert to
achieve its results (Fig. 1). In general, the registration procedure is
used to bring a brain atlas into alignment with an individual subject
scan being processed for brain extraction. The atlas contains
information regarding which brain extraction algorithm, or com-
bination of extractors, works best identifying brain in each ana-
tomic region. The overall best combination of brain extractors for
each region, based on a training set of scans and manual demarca-
tions of brain, is then applied on a voxel by voxel basis.
The extractors used in BEMA include the Brain Surface
Extractor (BSE) (Shattuck et al., 2001) from BrainSuite (Shattuck
and Leahy, 2002), the Brain Extraction Tool (BET) (Smith, 2002)
from FSL (Smith et al., 2001), 3dIntracranial (Ward, 1999) from
AFNI (Cox, 1996; Cox and Hyde, 1997), and MRI Watershed from
FreeSurfer (Dale et al., 1999). The volume registration procedure
utilized is FLIRT (Jenkinson and Smith, 2001), also from FSL. The
T1-weighted ICBM152 average MRI (Evans et al., 1994) in
approximate Talairach space (Talairach and Tournoux, 1988) is
utilized for the whole-head atlas space. All algorithms utilized are
freely available on the World Wide Web and have been encapsu-
lated in the LONI Pipeline Processing Environment (Rex et al.,
2003), along with utility functions from the Laboratory of Neuro
Imaging, AIR (Woods et al., 1998), and FSL.
Preprocessing of the data sets for each individual extraction
algorithm was performed to provide the best possible results from
each brain extractor. BEMA begins with a FLIRT registration of
the ICBM152 average to the individual subject scan. A brain mask
is resampled to the subject to identify a region that must contain the
whole brain. This brain mask consists of voxels in ICBM152 space
where any of 200 previously aligned subjects contained any brain
tissue. The subject scan is masked and cropped to limit the search
space for the subject’s brain. The resulting volume is passed, in
D.E. Rex et al. / NeuroImage 23 (2004) 625–637
This workwasgenerouslysupported bygrants fromtheNational
Institute of Mental Health (1 P20 MH65166 and 5 P01 MH52176)
and the National Center for Research Resources (2 P41 RR13642
and 2 M01 RR00865), with a supplement for the Biomedical
Informatics Research Network (2 P41 RR13642) (http://
www.nbirn.net/). DER is supported, in part, by an ARCS
authors wish to thank Drs. Robert Bilder, John Mazziotta, and
Tonmoy Sharma for providing the LIJMC, ICBM, and IPDH data
sets, respectively.The authors also wish to thank the members of the
Laboratory of Neuro Imaging for their help and support.
Alfano, B., Brunetti, A., et al., 1997. Unsupervised, automated segmenta-
tion of the normal brain using a multispectral relaxometric magnetic
resonance approach. Magn. Reson. Med. 37, 84–93.
Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry—The methods.
NeuroImage 11 (6 Pt 1), 805–821.
Baillet, S., Mosher, J., et al., 1999. Brainstorm: a Matlab toolbox for the
processing of MEG and EEG signals. NeuroImage 9, S246.
Bajcsy, R., Lieberson, R., et al., 1983. A computerized system for elastic
matching of deformed radiographic images to idealized atlas images.
J. Comput. Assist. Tomogr. 7, 618–625.
Bedell, B.J., Narayana, P.A., 1998. Volumetric analysis of white matter,
grey matter, and CSF using fractional volume analysis. Magn. Reson.
Med. 39, 961–969.
Boesen, K., Rehm, K., et al., 2003. Quantitative comparison of four brain
extraction algorithms. NeuroImage Abs. 19 (2) (CD-ROM).
Bomans, M., Hohne, K., et al., 1990. 3-D segmentation of MR images of
the head for 3-D display. IEEE Trans. Med. Imag. 9 (2), 177–183.
Brummer, M.E., Merseau, R.M., et al., 1993. Automatic detection of brain
contours in MRI data sets. IEEE Trans. Med. Imag. 12, 153–166.
Christensen, G.E., Rabbitt, R.D., et al., 1996. Deformable templates
using large deformation kinematics. IEEE Trans. Image Process. 5,
Collins, D.L., Holmes, C.J., et al., 1995. Automatic 3D model-based neuro-
anatomical segmentation. Hum. Brain Mapp. 3 (3), 190–208.
Collins, D.L., Zijdenbos, A.P., et al., 1999. ANIMAL+INSECT: improved
cortical structure segmentation. Proc. Annu. Symp. Inf. Process. Med.
Imag. 1613, 210–223.
Cox, R.W., 1996. AFNI: software for analysis and visualization of func-
tional magnetic resonance Neuroimages. Comput. Biomed. Res. 29 (3),
Cox, R.W., Hyde, J.S., 1997. Software tools for analysis and visualization
of fMRI data. NMR Biomed. 10 (4–5), 171–178 (pii).
Dale, A.M., Sereno, M.I., 1993. Improved localization of cortical activity
by combining EEG and MEG with MRI cortical surface reconstruction:
a linear approach. J. Cogn. Neurosci. 5, 162–176.
Dale, A.M., Fischl, B., et al., 1999. Cortical surface-based analysis: I.
Segmentation and surface reconstruction. NeuroImage 9 (2), 179–194.
Davatzikos, C., 1997. Spatial transformation and registration of brain
images using elastically deformable models. Comput. Vis. Image
Underst. 66, 207–222.
Evans, A.C., Collins, D.L., et al., 1994. Three-dimensional correlative
imaging: applications in Human brain mapping. In: Huerta, M. (Ed.),
Functional Neuroimaging: Technical Foundations. Academic Press, San
Diego, pp. 145–162.
Fennema-Notestine, C., Ozyurt, I.B., et al., 2003. Bias correction, pulse
sequence, and neurodegeneration influence performance of automated
skull-stripping methods. Society for Neuroscience, New Orleans.
Fischl, B., Sereno, M.I., et al., 1999. High-resolution intersubject averaging
and a coordinate system for the cortical surface. Hum. Brain Mapp.
8 (4), 272–284.
Held, K., Kops, E.R., et al., 1997. Markov random field segmentation of
brain MR images. IEEE Med. Imag. 16, 878–886.
Hohne, K.H., Hanson, W.A., 1992. Interactive 3D segmentation of MRI
and CT volumes using morphological operations. J. Comput. Assist.
Tomogr. 16 (2), 285–294.
Jenkinson, M., Smith, S., 2001. A global optimisation method for ro-
bust affine registration of brain images. Med. Image Anal. 5 (2),
Kapur, T., Grimson, W.E.L., et al., 1996. Segmentation of brain tissue from
magnetic resonance images. Med. Image Anal. 1, 109–127.
Lawson, J.A., Vogrin, S., et al., 2000. Cerebral and cerebellar volume
reduction in children with intractable epilepsy. Epilepsia 41 (11),
Lemieux, L., Hagemann, G., et al., 1999. Fast, accurate and reproducible
automatic segmentation of the brain in T1-weighted volume magnetic
resonance image data. Magn. Reson. Med. 42, 127–135.
Lemieux, L., Hammers, A., et al., 2003. Automatic segmentation of the
brain and intracranial cerebrospinal fluid in T1-weighted volume MRI
scans of the head, and its application to serial cerebral and intracranial
volumetry. Magn. Reson. Med. 49 (5), 872–884.
MacDonald, D., Avis, D., et al., 1994. Multiple surface identification and
matching in magnetic resonance images. Proc. Vis. Biomed. Comput.
MacDonald, D., Kabani, N., et al., 2000. Automated 3-D extraction of
inner and outer surfaces of cerebral cortex from MRI. NeuroImage 12
Miller, M.I., Christensen, G.E., et al., 1993. Mathematical textbook of de-
formable neuroanatomies. Proc. Natl. Acad. Sci. 90 (24), 11944–11948.
Rehm, K., Shattuck, D., et al., 1999. Semi-automated stripping of T1 MRI
volumes: I. Consensus of intensity- and edge-based methods. Neuro-
Image Abs. 9 (6), S86.
Rex, D.E., Ma, J.Q., et al., 2003. The LONI pipeline processing environ-
ment. NeuroImage 19 (3), 1033–1048.
Sandor, S., Leahy, R., 1997. Surface-based labeling of cortical anatomy
using a deformable database. IEEE Trans. Med. Imag. 16, 41–54.
Schalkoff, R.J., Shaaban, K.M., 1999. Image processing meta-algorithm
development via genetic manipulation of existing algorithm graphs.
SPIE Proc. Vis. Inf. Process. VIII 3716, 61–70.
Schroder, T., Rosler, U., et al., 1999. Optimizing deconvolution techniques
by the application of the Munchhausen meta algorithm. Biomed. Tech.
(Berl) 44 (11), 308–313.
Shaaban, K.M., Schalkoff, R.J., 1995. Image processing and comput-
er vision algorithm selection and refinement using an operator-
assisted meta-algorithm. SPIE Proc. Vis. Inf. Process. IV 2488,
Shattuck, D.W., Sandor-Leahy, S.R., et al., 2001. Magnetic resonance im-
age tissue classification using a partial volume model. NeuroImage 13
Shattuck, D.W., Leahy, R.M., 2002. BrainSuite: an automated cortical
surface identification tool. Med. Image Anal. 6 (2), 129–142.
Smith, S.M., 2002. Fast robust automated brain extraction. Hum. Brain
Mapp. 17 (3), 143–155.
Smith, S., Bannister, P., et al., 2001. FSL: new tools for functional and
structural brain image analysis. Seventh International Conference on
Functional Mapping of the Human Brain, Brighton, UK, NeuroImage
Smith, S.M., Zhang, Y., et al., 2002. Accurate, robust, and automated
longitudinal and cross-sectional brain change analysis. NeuroImage
17 (1), 479–489.
Talairach, J., Tournoux, P., 1988. Co-Planar Stereotaxic Atlas of the Human
Brain. Thieme, New York.
Thompson, P.M., Mega, M.S., et al., 2001. Cortical change in Alzheimer’s
disease detected with a disease-specific population-based brain atlas.
Cereb. Cortex 11 (1), 1–16.
D.E. Rex et al. / NeuroImage 23 (2004) 625–637
of Wisconsin: http://afni.nimh.nih.gov/pub/dist/doc/3dIntracranial.pdf.
Woods, R.P., Mazziotta, J.C., et al., 1993. MRI-PET registration with au-
tomated algorithm. J. Comput. Assist. Tomogr. 17 (4), 536–546.
Woods, R.P., Grafton, S.T., et al., 1998. Automated image registration: II.
Intersubject validation of linear and nonlinear models. J. Comput. As-
sist. Tomogr. 22 (1), 153–165.
Zhang, Y., Brady, M., et al., 2001. Segmentation of brain MR images
through a hidden Markov random field model and the expectation–
maximization algorithm. IEEE Trans. Med. Imag. 20 (1), 45–57.
D.E. Rex et al. / NeuroImage 23 (2004) 625–637