Bibliography of Segmentation in Neuroimaging

Finn Årup Nielsen
CIMBI at DTU Informatics and NRU Rigshospitalet
Lyngby and Copenhagen, Denmark

  $Revision: 1.75 $
  $Date: 2009/03/11 15:24:52 $


Reference for segmentation in neuroimaging are collected. Both tissue segmentation and parcellation is included.

This structured bibliography is part of a larger collection of bibliographies see The bibliography is written in LATEX and BIBTeX and should be available both as HTML and PostScript.

The bibliography is probably far from complete, but new references are added whenever the author finds new material and has the time to add them. You can email the author if corrections are required or you have found some reference that you fell ought to be included:

Thanks to Jürgen Hänggi, Jonathan Bailleul and Arno Klein who provided information. Much of the information in this bibliography is from the SPM mailing list posted by numerous researchers.

This work is or has been funded by the European Union project MAPAWAMO, the International Neuroimaging Consortium (INC) American Human Brain Project, Danish Research Councils through THOR Center for Neuroinformatics, the Villum Kann Rasmussen Foundation and the Lundbeck Foundation.


List of Tables

  1. Inhomogeneity correction tools
  2. Stripping
  3. Methods for segmentation
  4. Tools for segmentation
  5. Cortical surface extraction
  6. Flattening algorithms
  7. Parcellation tools
  8. Parcellation tools
  9. Labeled brains

General references

A list of references is available from


[Harris et al., 2001] parcellation of cortex with brain warping and manual atlas. PMID: 8978636, PMID: 9786148, PMID: 11185422

B. Dawant, S.L. Hartmann, J.-P. Thirion, F. Maes, D. Vandermeulen, P. Demaerel, Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations : part I, methodology and validation on normal subjects , IEEE transactions on medical imaging, vol. 18, no. 10, pp. 909-916, October 1999

Inhomogeneity correction

MRI intensity non-uniformity (intensity inhomogeneity) can have a substantial impact on the performance of the segmentation results [Sled et al., 1997a] and the image should be bias field corrected. Table 1 shows a number of the tools in use. [Ashburner and Friston, 1998] describes a combination of tissue classification with inhomogeneity correction.

[Arnold et al., 2001,Schaper et al., 2001] compared six algorithms for inhomogeneity correction (N3, hum, eq, bfc, cma and SPM99). [Boyes et al., 2008] investigated the performance of the N3 program on scans from 3T scanners. Other references in relation to bias field estimation are [Guillemaud and Brady, 1997] and a review [Hou, 2006].

Table 1: MRI Inhomogeneity correction tools.
Name Method and Description References
cma   Center for Morphometric Analysis, Massachusetts General Hospital
EMS * Polynomial basis functions. Part of segmentation program [Van Leemput, 2001, p. 15-19], [Van Leemput et al., 1999a]
eq   [Cohen et al., 2000]
FAST * ``FMRIB's Automated Segmentation Tool''. A segmentation tool including inhomobeneity correction
FreeSurfer * Implemented in the mri_normalize program. Can be executed from csurf GUI. [Dale et al., 1999,Fischl et al., 1999a,Fischl et al., 1999b,Fischl and Dale, 2000,Fischl et al., 2001,Busa, 2002]
hum   [Brinkmann et al., 1998]
ITK * The ``itk::MRIBiasFieldCorrectionFilter'' class in the National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) based on Legendre polynomial, [Styner et al., 2000,Styner and Gerig, 1997]
N3 *   [Sled et al., 1998,Sled et al., 1997b,Sled, 1997],,
PABIC * ``PArametric BIas field Correction''. Included in ITK. [Styner et al., 2000,Styner and Gerig, 1997]
SPM *   [Ashburner and Friston, 2000]
SPM2 * Available in the functions with prefix spm_bias_ [Ashburner, 2002], Early version:
vol_homocor.m * Program by Gary Glover distributed by Kalina Christoff kalina/SPM99/Tools/vol_homocor.html


The process of ``stripping'', ``skull-stripping'', ``brain/non-brain segmentation'', ``brain surface extraction'', ``brain extraction'' or ``brain extraction algorithms (BEA)'' removes the skull, scalp and meninges and maintains the ``brain'' which usually includes white and grey matter as well as CSF (at least the ventricular CSF). Table 2 is a list of the tools for this operation. A study showed that McStrip was much slower than BSE and BET, but that it was the most precise [Boesen et al., 2003]. In a comparison BET, 3dIntracranial, HWA and BSE against a manual stripping as gold standard ``BSE tended to perform best'' and ``HWA and BSE were more robust across diagnostic groups'' [Fennema-Notestine et al., 2006]. Another algorithm is described in [Atkins and Mackiewich, 1998].

One study found that in voxel-based morphometry (VBM) using SPM2 brain extraction would profoundly affect the results [Fein et al., 2006].

Table 2: Stripping
Name Impl. Description Reference
3dIntracranial *   Brain extraction included in AFNI [Ward, 1999],
BEMA   ``Brain extraction meta-algorithm'' [Rex et al., 2004]
BET *   ``Brain Extraction Tool'' by Stephen Smith. Conveniently included in FSL, MRIcro and mri3dX [Smith, 2002,Smith, 2000],, MRIcro:
BSE *   ``Brain Surface Extraction''part of BrainSuite. Interactive GUI version exists with the X/Motif-based xbse [Shattuck et al., 2001,Sandor and Leahy, 1997],
McStrip * IDL, C ``Minneapolis Consensus Strip'' (MCS). Consensus/hybrid based method relying on AIR5.0 and BSE [Rehm et al., 2004,Rehm et al., 1999],
MIPAV * Java   [Bazin et al., 2007,Goldszal et al., 1998],
FreeSurfer *   Can be executed from csurf GUI. [Dale et al., 1999,Fischl et al., 1999a,Fischl et al., 1999b,Fischl and Dale, 2000,Fischl et al., 2001,Busa, 2002]
HWA *   Hybrid Watershed algorithm in FreeSurfer [Segonne et al., 2004]

Brain tissue segmentation

Brain tissue segmentation typically classifies voxels into grey matter, white matter, CSF and ``non-brain''. Some segmentations works with a further ``lesion'' class.

Methods for segmentation

Many papers describe methods for brain tissue segmentation, and just a few are listed in Table 3. Others are [Cocosco et al., 2002,Sun and Wang, 2005].

Table 3: Methods for segmentation.
Description Reference
Use of atlas prior (tissue probability maps in stereotaxic space) [Kamber et al., 1995]
Input as T1, T2, PD and output as GM, WM, CSF. Selection of a training classes for the segmentation [Harris et al., 1999]
Input as T1, T2, PD and output as WM, GM, CSF, outliers. With inhomogeneity correction and atlas prior. Gaussian mixture estimated robustly. Lesions detected as outliers. Bias field modeled with polynomials. Markov random field for prior volumes [Van Leemput et al., 2001,Van Leemput et al., 2000]
K-Nearest Neighbor on data from five types of regular MRI-scans for classification of white matter lesions [Anbeek et al., 2003]
``Fuzzy inference system'' on 3 different MR images for classifying white matter hyperintensity [Admiraal-Behloul et al., 2005]
Support vector machine on 4 different MR images for white matter lesion segmentation [Lao et al., 2006]

A ``ground truth'' makes it possible to evaluate the performance of the segmentation algorithm. [Moretti et al., 2000] took this approach with the use of the BrainWeb labeled brain as ground truth.


Table 4: Tools for segmentation
Name Input Output Description Reference
BrainSeg       Ali Hojjat
EMS     `Expectation-Maximization Segmentation' implemented as an SPM plugin [Van Leemput et al., 2001,Van Leemput et al., 1999b,Van Leemput et al., 1999a,Maes et al., 1997,Van Leemput et al., 2000]
FAST *   GM, WM, CSF, ... FMRIB's Automated Segmentation Tool, Hidden Markov model with inhomogeneity correction [Zhang et al., 2001a,Zhang et al., 2000,Zhang et al., 2001b]
INSECT     GM, WM and CSF segmentation with an artificial neural network with 9-parameter spatial normalization [Kollokian, 1996,Collins et al., 1994]
IRIS *     Visualization program with manual drawing by Guido Gerig and Sean Ho. One of the versions is called IRIS2000
MIDAS (Freeborough)     ``Medical Image Display and Analysis Software''. Interactive Unix/X program, with thresholding, region growing and morphological operations [Freeborough et al., 1997]
SEAL     ``Sulcal Extraction and Automated Labelling'' [Goualher et al., 1999]
SEGRAS   WM, GM, CSF, Lesion Trained artificial neural network used as classifier Alan Rene Rasmussen, Hvidovre Hospital
SPM *     Segments into GM, WM, CSF and other. Implemented in versions SPM99 and SPM2. [Ashburner and Friston, 1997,Ashburner and Friston, 2000,Ashburner and Friston, 2003]
SPM5 *     Segmentation with image registration and bias correction [Ashburner and Friston, 2005]
--     Combined manual/automatic [Zavaljevski et al., 2000]

Labeled brains

Probalistic volumes for background, CSF, grey matter, white matter, fat, muscle/skin, skin, skull, glial matter, and ``connective'' are available in connection with the BrainWeb web-service/database from the URL [Cocosco et al., 1997].

``ICBM tissue probabilities'' with gray matter, white matter and CSF are available from

The Internet Brain Segmentation Repository (IBSR),, has simulated and real MRI data with gray/white/other expert segmentations.

Gray, white and CSF and brain mask are also distributed with the SPM2 package (in the apriori subdirectory).

Cortical surface extraction

``Cortical surface extraction'' or ``Cortical surface reconstruction''.

The ``marching cubes'' algorithm [Lorensen and Cline, 1987] can extract the cortical surface but usually with a bad results, e.g., not necessarily topologically correct. The algorithm is implemented in Matlab, IDL, VTK and polyr [Jensen, 1995,Nielsen, 1998].

[Mohlberg and Zilles, 2000] obtains somewhat better results by combining surface warping, marching cubes and a fluid membrane model. [Zeng et al., 1999,MacDonald et al., 2000] use coupled inner and outer surface of the cortex. [Goldenberg et al., 2002] describes an other method. None of these seem to be publicly available. The MacDonald program seems to be available internally at MNI and able to handle MINC files, see

FreeSurfer traces the white matter [Dale et al., 1999]. A poor man's method with a MRI T1 along this line is first to do skull-stripping, then threshold on a sufficiently high value to only incorporate the white matter and lastly make an ordinary marching cubes. [Schaper et al., 2006] perform a quantitative comparison between four of the algorithms.

Table 5: Cortical surface extraction
Name Impl. Description Reference
BrainVisa     [Cointepas et al., 2001]
FreeSurfer *   Can be executed from csurf GUI. [Dale et al., 1999,Fischl et al., 1999a,Fischl et al., 1999b,Fischl and Dale, 2000,Fischl et al., 2001,Busa, 2002]
Geometrical Atlas Visualizer MacOS Visualization of the cortical surface on a disc and where the principal sulci and landmarks are aligned with the the axes. [Toro, 2003],
IsoSurf *   Isosurface
Polyr * C Marching cube [Jensen, 1995,Nielsen, 1998] ,
SureFit *
SurfRelax *     [Larsson, 2001],


After extraction of the surface algorithms can smooth it, into a sphere or cut and flatten it (unfold it) [Sherk, 1992,Carman et al., 1995,Van Essen and Maunsell, 1980,Jouandet et al., 1989]. Several of the tools in Table 5 have these capabilities.

Table 6: Flattening algorithms
Name Impl. Description Reference
DMflatten     [Balasubramanian et al., 2005,Balasubramanian et al., 2006]
mrFlatMesh Matlab Companion to mrGray A. R. Wade


IBASPM (``Individual Brain Atlases using Statistical Parametric Mapping software'' , is an SPM2 plugin that utilizes the normalization and brain tissue segmetation parts of SPM2 together with the AAL atlas for the construction of parcellation of the brain in individuals. Programs by Claus Svarer and others ( provide similar capabilities [Svarer et al., 2005,Svarer et al., 2002] using, e.g., MRIWarp [Kjems et al., 1999a,Kjems et al., 1999b]

Rview contains a number of interactive drawing functions

[Schleicher et al., 1999,Schleicher et al., 2000,Schmitt et al., 2003] describe methods for parcellation based on cytoarchitectonics. Macaque cortex parcellation based receptor binding density across multiple ligands is performed with different multivariate analysis techniques in [Kötter et al., 2001].


Table 7: Parcellation tools
Name Description Reference
ANIMAL `Automatic Non-linear Image Matching and Anatomical Labeling' Nonlinear warping and labeling by a previous labeled volume [Collins et al., 1995], ?
Mindboggle Automatic labeling based on 20 labeled templates [Klein et al., 2005,Klein and Hirsch, 2005,Klein and Hirsch, 2001]
pveout Nonlinear alignment with MRIWarp to 10 different labeled templates [Svarer et al., 2005]

Table 8: Mask and region tools
Name Description Reference
SimpleROIBuilder SPM2 and SPM5 toolbox
SPM Anatomy toolbox

Labeled brains

Table 9: Labeled brains. The second column with the `#' heading indicates the number of labels. `*' denotes that the labeled brain is readily available on the Internet. Entries above the line is digitized and paper atlasses are below the line.
Name # Description Reference
AAL   See Tzourio-Mazoyer  
Brodmann * 41+1 Brodmann areas. Non-space filling, non-probabilistic. Van Essen, Drury. Included in MRIcro as brodmann.hdr/brodmann.img(.gz)
CBA ``almost 400'' Atlas incorporated in a commercial program. ``Greitz atlas''. Brodmann areas, gyri, central structures [Applied Medical Imaging, 1994,Seitz et al., 1990,Greitz et al., 1991,Thurfjell et al., 1994,Thurfjell et al., 1995,Bohm et al., 1986,Bohm et al., 1989,Bohm et al., 1991,Bohm et al., 1985]
Cerefy   Commercial digitized versions of the Talairach and Schaltenbrand atlases and Windows/MacIntosh program. [Nowinski et al., 2001,Nowinski et al., 1997,Nowinski et al., 1995b,Nowinski et al., 1995a]
Hammers 2002 2 $ \times$ 19 + 3(?), 43 Segmentation of MNI single subject Non-probabilistic, space-filling, Non-hierarchical. [Hammers et al., 2002]
IBSR ``18'' 43 ``18 Scans: T1-weighted MR Image data with expert segmentations of 43 individual structures''
ICBM label *(?) 58(?) ICBM Single subject MRI anatomical template. Distributed in Minc format (ICBM_labels.mnc and ICBM_1.0mm_label.mnc). Almost space-filling. Non-hierarchical., Label names:
ICBM Kabani 90/91(?) Parcellation of MNI single subject in accordance with NeuroNames [Kabani et al., 1998]
`Iowa' (frontal) 11 Parcellations of the frontal cortex by two human raters [Crespo-Facorro et al., 1999]
`Iowa' (temporal) 16 Parcellation of the temporal neocortex [Kim et al., 2000]
`Iowa' (cerebral cortex) 41   [Crespo-Facorro et al., 2000]
Mindboggle * ? Based on 10-20 subjects. Previously `The Whole Brain Atlas' transformed to MNI-space [Klein and Hirsch, 2005,Klein and Hirsch, 2001,Klein and Hirsch, 2002,Kikinis et al., 1996],
MNI SPAM 91 Probabilistic volumes in MNI-space [Evans et al., 1996,Collins et al., 1999]
Jerne, ``Volumes of Interest'') * 100+ MNI-space, probabilistic, space-filled, hierarchical. Approximate volumes based on labeling in the BrainMap database. [Nielsen and Hansen, 2002],
PMaps * 18 Probability map of selected cytoarchitectonic areas from Jülich: 1, 3a, 3b, 4a, 4p, 6, 17 (V1), 18 (V2), 41, 44, 45 [Eickhoff et al., 2005c,Eickhoff, 2005,Geyer et al., 1996,Amunts et al., 1999,Amunts et al., 2000,Morosan et al., 2001,Rademacher et al., 2001,Amunts et al., 1998,Morosan et al., 1996,Eickhoff et al., 2005b,Eickhoff et al., 2005a],,
Svarer * $ 2\times 17+1$ 10 subjects labeled in native space [Svarer et al., 2005,Svarer et al., 2002],
Talairach Daemon *   A program that contains two brain templates: A digitized Talairach atlas and MNI [Lancaster et al., 2000b,Lancaster et al., 1997a,Lancaster et al., 1997b,Lancaster et al., 2000a]. The labeling is used by the WFU PickAtlas program.
Tzourio-Mazoyer * 2 $ \times$ 45(?), 116 MNI-space, non-probabilistic, non-space filled, semi-hierarchical. This is sometimes referred to as ``automated anatomical labeling'' or ``AAL''. The labeled volume is distributed with MRIcro has 116 different labels [Tzourio-Mazoyer et al., 2002a,Tzourio-Mazoyer et al., 2002b], The labeled volume is distributed with MRIcro as aal.hdr/aal.img(.gz) and aal.txt
VOXEL-MAN ? Commercial program with atlas [Höhne et al., 1992,Höhne, 1997,Höhne, 2001]
Brodmann   Book with simple drawings of cytoarchitectonic areas. [Brodmann, 1994]
      Duvernoy 1992
Mai   Book with labeled brain in stereotaxic space [Mai et al., 1997]
Ono   Book that describe sulcal variability [Ono et al., 1990]
Schaltenbrand   Book [Schaltenbrand and Wahren, 1977]
      Szikla et al. 1977
Talairach `Many' Book with a labeled brain in stereotaxic space [Talairach and Tournoux, 1988,Talairach and Szikla, 1967]

Anatomical labeled brains in stereotaxic coordinates can form the basis for automatic labeling new brains and coordinates. A bibliography on brain atlases is also available at Early swedish effort is documented in [Bohm et al., 1985,Bohm et al., 1991,Greitz et al., 1991,Greitz et al., 1995]. Early montrealean in [Evans et al., 1988,Evans et al., 1991].

Brodmann area labeling has been developed using descriptions from the Caret package on the Colin27 atlas and a cortical surface matching method [Rasser et al., 2004]: Individual subject's structural MRI are deformed to an atlas.

An Internet service with the MNI SPAM probability volumes was announced with [Kim et al., 2002] with the URL but it does not seem to work.


[Bajcsy et al., 1983,Bailleul et al., 2004]

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Admiraal-Behloul et al., 2005
Admiraal-Behloul, F., van den Heuvel, D. M. J., Olofsen, H., van Osch, M. J. P., J. van der Grond, M. A. v. B., and Reiber, J. (2005).
Fully automatic segmentation of white matter hyperintensities in MR images of the elderly.
NeuroImage, 28(3):607-617. DOI: 10.1016/j.neuroimage.2005.06.061.

Amunts et al., 1998
Amunts, K., Klingberg, T., Binkofski, F., Schormann, T., Seitz, R. J., Roland, P. E., and Zilles, K. (1998).
Cytoarchitectonic definition of Broca's region and it's role in functions different from speech.
In Paus, T., Gjedde, A., and Evans, A. C., editors, Fourth International Conference on Functional Mapping of the Human Brain. NeuroImage, volume 7, page S8. Academic Press. ISSN 1053-8119 [ ] .

Amunts et al., 2000
Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., and Zilles, K. (2000).
Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable?
NeuroImage, 11(1):66-84. PMID: 10686118.

Amunts et al., 1999
Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H. B., and Zilles, K. (1999).
Broca's region revisited: cytoarchitecture and intersubject variability.
Journal of Comparative Neurology, 412(2):319-341. PMID: 10441759.

Anbeek et al., 2003
Anbeek, P., Vincken, K. L., van Osch, M. J. P., Bisschops, B., Viergever, M. A., and van der Grond, J. (2003).
Automated white matter lesion segmentation by voxel probability estimation.
In Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2003, volume 2878 of Lecture Notes in Computer Science, pages 610-617, Berlin/Heidelberg. Springer. ISBN 0302-9743 [ | ] .

Applied Medical Imaging, 1994
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CBA User's Manual.
Applied Medical Imaging, Uppsala, Sweden, 3.1 edition.

Arnold et al., 2001
Arnold, J. B., Liow, J.-S., Schaper, K. A., Stern, J. J., Sled, J. G., Shattuck, D. W., Worth, A. J., Cohen, M. S., Leahy, R. M., Mazziotta, J. C., and Rottenberg, D. A. (2001).
Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects.
NeuroImage, 13(5):931-943. PMID: 11304088.

Ashburner, 2002
Ashburner, J. (2002).
Another MRI bias correction approach.
NeuroImage, 16(2).
Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-Rom.

Ashburner and Friston, 1997
Ashburner, J. and Friston, K. J. (1997).
Multimodal image coregistration and partitioning -- a unified framework.
NeuroImage, 6(3):209-217. PMID: 9344825.

Ashburner and Friston, 1998
Ashburner, J. and Friston, K. J. (1998).
MRI sensitivity correction and tissue classification.
NeuroImage, 7(4):S706.

Ashburner and Friston, 2000
Ashburner, J. and Friston, K. J. (2000).
Voxel-based morphometry - the methods.
NeuroImage, 11:805-821.

Ashburner and Friston, 2003
Ashburner, J. and Friston, K. J. (2003).
Image segmentation.
In Frackowiak, R. S. J., Friston, K. J., Dolan, R., and Price, C., editors, Human Brain Function. Academic Press, second edition. ISBN 0122648412 [ | ] .

Ashburner and Friston, 2005
Ashburner, J. and Friston, K. J. (2005).
Unified segmentation.
NeuroImage, 26(3):839-851. PMID: 15955494. DOI: 10.1016/j.neuroimage.2005.02.018.

Atkins and Mackiewich, 1998
Atkins, M. S. and Mackiewich, B. T. (1998).
Fully automatic segmentation of the brain in MRI.
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Bailleul et al., 2004
Bailleul, J., Ruan, S., Bloyet, D., and Romaniuk, B. (2004).
Segmentation of anatomical structures from 3D brain MRI using automatically-built statistical shape models.
In IEEE International Conference on Image Processing. IEEE Signal Processing Society.

Bajcsy et al., 1983
Bajcsy, R., Lieberson, R., and Reivich, M. (1983).
A computerized system for the elastic matching of deformed radiographic images to idealised atlas images.
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Balasubramanian et al., 2005
Balasubramanian, M., Polimeni, J. R., and Schwartz, E. L. (2005).
Efficient quasi-isometric flattening of large-scale cortical surfaces.
NeuroImage, 26(1):S751.

Balasubramanian et al., 2006
Balasubramanian, M., Polimeni, J. R., and Schwartz, E. L. (2006).
Quantitative evaluation and comparison of cortical flattening algorithms.
In Neuroscience. Society for Neuroscience.

Bazin et al., 2007
Bazin, P.-L., Cuzzocreo, J. L., Yassa, M. A., Gandler, W., McAuliffe, M. J., Bassett, S. S., and Pham, D. L. (2007).
Volumetric neuroimage analysis extension for the MIPAV software package.
Journal fo Neuroscience Methods, 165(1):111-121.

Boesen et al., 2003
Boesen, K., Rehm, K., Schaper, K., Stoltzner, S., Woods, R., and Rottenberg, D. A. (2003).
Quantitative comparison of three brain extraction algorithms.
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Presented at the 9th International Conference on Functional Mapping of the Human Brain, June 19-22, 2003, New York, NY. Available on CD-Rom.

Bohm et al., 1986
Bohm, C., Greitz, T., Blomqvist, G., Farde, L., Forsgren, P. O., Kingsley, D., and Sjogren, I. (1986).
Applications of a computerized adjustable brain atlas in positron emission tomography.
Acta Radiologica, 369:449-452.

Bohm et al., 1989
Bohm, C., Greitz, T., and Eriksson, L. (1989).
A computerized adjustable brain atlas.
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Bohm et al., 1985
Bohm, C., Greitz, T., Kingsley, D., Berggren, B. M., and Olsson, L. (1985).
A computerized individually variable stereotaxic brain atlas.
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Bohm et al., 1991
Bohm, C., Greitz, T., Seitz, R., and Eriksson, L. (1991).
Specification and selection of regions of interest (ROIs) in a computerized atlas.
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Boyes et al., 2008
Boyes, R. G., Gunter, J. L., Frost, C., Janke, A. L., Yeatman, T., Hill, D. L., Bernstein, M. A., Thompson, P. M., Weiner, M. W., Schuff, N., Alexander, G. E., Killiany, R. J., DeCarli, C., Jack, C. R., and Fox, N. C. (2008).
Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils.
NeuroImage, 39(4):1752-1762. DOI: 10.1016/j.neuroimage.2007.10.026.

Brinkmann et al., 1998
Brinkmann, B. H., Manduca, A., and Robb, R. A. (1998).
Optimized homomorphic unsharp masking for mr grayscale inhomogeneity correction.
IEEE Transactions on Medical Imaging, 17(2):161-171. PMID: 9688149.

Brodmann, 1994
Brodmann, K. (1909/1994).
Brodmann's `Localisation in the Cerebral Cortex'.
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Busa, 2002
Busa, E. (2002).
FreeSurfer Manual.
Massachusetts General Hospital, Boston, Massachusetts.

Carman et al., 1995
Carman, G. J., Drury, H. A., and Van Essen, D. C. (1995).
Computational methods for reconstructing and unfolding the cerebral cortex.
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Cocosco et al., 1997
Cocosco, C. A., Kollokian, V., Kwan, R. K.-S., and Evans, A. C. (1997).
BrainWeb: Online interface to a 3D MRI simulated brain database.
In [Friberg et al., 1997]. ISSN 1053-8119 [ ] .

Cocosco et al., 2002
Cocosco, C. A., Zijdenbos, A. P., and Evans, A. C. (2002).
Automatic generation of training data for brain tissue classification from MRI.
In Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2002, volume 2488 of Lecture Notes in Computer Science, Berlin/Heidelberg. Springer.

Cohen et al., 2000
Cohen, M. S., DuBois, R. M., and Zeneih, M. M. (2000).
Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging.
Human Brain Mapping, 10:204-211.

Cointepas et al., 2001
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Parcellation | Labeled brains | Labeled brains
automated anatomical labeling
Labeled brains
brain extraction
Labeled brains
Cortical surface extraction
Cortical surface extraction
Methods for segmentation | Labeled brains
Labeled brains | Labeled brains | Labeled brains
Labeled brains
Labeled brains
Labeled brains
Inhomogeneity correction | Tools
no title
Inhomogeneity correction | Stripping | Cortical surface extraction
Geometric Atlas
Cortical surface extraction
ground truth
Methods for segmentation
Labeled brains
Labeled brains | Labeled brains
Labeled brains | Labeled brains
Internet Brain Segmentation Repository
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Cortical surface extraction
Inhomogeneity correction
Labeled brains
Labeled brains
labeled brain
no title
Labeled brains
marching cubes
Cortical surface extraction
Tools | Labeled brains
Inhomogeneity correction
Stripping | Labeled brains | Labeled brains
Inhomogeneity correction
Labeled brains
Inhomogeneity correction
no title
Labeled brains
Cortical surface extraction | Cortical surface extraction
no title
brain tissue
no title
Inhomogeneity correction | Tools
SPM Anatomy toolbox
Inhomogeneity correction | Tools | Labeled brains | Parcellation
no title | Stripping
Cortical surface extraction
Cortical surface extraction
Labeled brains
Labeled brains
Talairach Daemon
Labeled brains
Labeled brains
voxel-based morpometry
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Cortical surface extraction

Finn Årup Nielsen 2010-04-23