Skip to main content

Advertisement

SpringerLink
Log in

Segmentation of human brain using structural MRI

  • Review Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Lim KO, Pfefferbaum A (1989) Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. J Comput Assist Tomogr 13:588–593

    Article  CAS  PubMed  Google Scholar 

  2. Ashburner J (2012) SPM: a history. Neuroimage 62:791–800

    Article  PubMed  Google Scholar 

  3. Lucas B, Bogovic J, Carass A, Bazin P-L, Prince J, Pham D, Landman B (2010) The Java image science toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 18:5–17

    Article  Google Scholar 

  4. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward SR, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341

    Article  PubMed  PubMed Central  Google Scholar 

  5. Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903–921

    Article  PubMed  PubMed Central  Google Scholar 

  6. Koenig SH, Brown RD 3rd, Spiller M, Lundbom N (1990) Relaxometry of brain: why white matter appears bright in MRI. Magn Reson Med 14(3):482–495

    Article  CAS  PubMed  Google Scholar 

  7. Kamman RL, Go KG, Brouwer W, Berendsen HJ (1988) Nuclear magnetic resonance relaxation in experimental brain edema: effects of water concentration, protein concentration, and temperature. Magn Reson Med 6(3):265–274

    Article  CAS  PubMed  Google Scholar 

  8. Gelman N, Gorell JM, Barker PB, Savage RM, Spickler EM, Windham JP, Knight RA (1999) MR imaging of human brain at 3.0 T: preliminary report on transverse relaxation rates and relation to estimated iron content. Radiology 210(3):759–767

    Article  CAS  PubMed  Google Scholar 

  9. Gelman N, Ewing JR, Gorell JM, Spickler EM, Solomon EG (2001) Interregional variation of longitudinal relaxation rates in human brain at 3.0 T: relation to estimated iron and water contents. Magn Reson Med 45(1):71–79

    Article  CAS  PubMed  Google Scholar 

  10. Hallgren B, Sourander P (1958) The effect of age on the non-haemin iron in the human brain. J Neurochem 3(1):41–51

    Article  CAS  PubMed  Google Scholar 

  11. Helms G, Kallenberg K, Dechent P (2006) A contrast-driven approach to intracranial segmentation using a combination of T2- and T1-weighted 3D MRI datasets. J Magn Reson Imaging 24(4):790–795

    Article  PubMed  Google Scholar 

  12. Deichmann R, Good CD, Josephs O, Ashburner J, Turner R (2000) Optimization of 3-D MP-RAGE sequences for structural brain imaging. Neuroimage 12:112–127

    Article  CAS  PubMed  Google Scholar 

  13. Deichmann R, Schwarzbauer C, Turner R (2004) Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T. NeuroImage 21:757–767

    Article  CAS  PubMed  Google Scholar 

  14. Jack CJ, Bernstein M, Fox N, Thompson P, Alexander G, Harvey D, Borowski B, Britson P, Whitwell J, Ward C, Dale A, Felmlee J, Gunter J, Hill D, Killiany R, Schuff N, Fox-Bosetti S, Lin C, Studholme C, DeCarli C, Krueger G, Ward H, Metzger G, Scott K, Mallozzi R, Blezek D, Levy J, Debbins J, Fleisher A, Albert M, Green R, Bartzokis G, Glover G, Mugler J, Weiner M (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27(4):685–691

    Article  PubMed  PubMed Central  Google Scholar 

  15. Alfano B, Brunetti A, Covelli EM, Quarantelli M, Panico MR, Ciarmiello A, Salvatore M (1997) Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach. Magn Reson Med 37(1):84–93

    Article  CAS  PubMed  Google Scholar 

  16. Sled JG, Zijdenbos AP, Evans AC (1998) A non-parametric method for automatic correction of intensity non-uniformity in MRI. IEEE Trans Med Imag 17:87–97

    Article  CAS  Google Scholar 

  17. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imag 29(6):1310–1320

    Article  Google Scholar 

  18. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation–maximization algorithm. IEEE Trans Med Imaging 20(1):45–57

    Article  CAS  PubMed  Google Scholar 

  19. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155

    Article  PubMed  Google Scholar 

  20. Iglesias JE, Liu CY, Thompson PM, Tu Z (2011) Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging 30:1617–1634

    Article  PubMed  Google Scholar 

  21. Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22(3):1060–1075

    Article  PubMed  Google Scholar 

  22. Carass A, Cuzzocreo J, Wheeler MB, Bazin PL, Resnick SM, Prince JL (2011) Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. Neuroimage 56(4):1982–1992

    Article  PubMed  PubMed Central  Google Scholar 

  23. Mikheev A, Nevsky G, Govindan S, Grossman R, Rusinek HJ (2008) Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm. J Magn Reson Imaging 27(6):1235–1241

    Article  PubMed  Google Scholar 

  24. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM (2009) Bayesian analysis of neuroimaging data in FSL. Neuroimage 45:S173–S186

    Article  PubMed  Google Scholar 

  25. Ashburner J, Friston K (1997) Multimodal image coregistration and partitioning—a unified framework. Neuroimage 6(3):209–217

    Article  CAS  PubMed  Google Scholar 

  26. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851

    Article  PubMed  Google Scholar 

  27. Evans AC, Kamber M, Collins DL, Macdonald D (1994) An MRI-based probabilistic atlas of neuroanatomy. In: Shorvon S, Fish D, Andermann F, Bydder GM, Stefan H (eds) Magnetic resonance scanning and epilepsy, NATO ASI series A, life sciences, vol 264. Plenum, New York, pp 263–274

    Chapter  Google Scholar 

  28. van Leemput K, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging 18(10):897–908

    Article  PubMed  Google Scholar 

  29. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004) Neuroplasticity: changes in grey matter induced by training. Nature 427(6972):311–312

    Article  CAS  PubMed  Google Scholar 

  30. Focke NK, Helms G, Kaspar S, Diederich C, Tóth V, Dechent P, Mohr A, Paulus W (2011) Multi-site voxel-based morphometry—not quite there yet. Neuroimage 56(3):1164–1170

    Article  CAS  PubMed  Google Scholar 

  31. Lambert C, Lutti A, Helms G, Frackowiak R, Ashburner J (2013) Multiparametric brainstem segmentation using a modified multivariate mixture of gaussians. Neuroimage Clin 16(2):684–694

    Article  Google Scholar 

  32. Bazin P-L, Pham D (2007) Topology correction of segmented medical images using a fast marching algorithm. Comput Methods Programs Biomed 88:182–190

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bazin P-L, Pham D (2008) Homeomorphic brain image segmentation with topological and statistical atlases. Med Image Anal 12:616–625

    Article  PubMed  PubMed Central  Google Scholar 

  34. Brownstein KR, Tarr CE (1977) Spin-lattice relaxation in a system governed by diffusion. J Magn Reson 26:17–24

    CAS  Google Scholar 

  35. Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97(20):11050–11055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kim JS, Singh V, Lee JK, Lerch J, Ad-Dab’bagh Y, MacDonald D, Lee JM, Kim SI, Evans AC (2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27(1):210–221

    Article  PubMed  Google Scholar 

  37. Han X, Pham D, Tosun D, Rettmann M, Xu C, Prince J (2004) CRUISE: cortical reconstruction using implicit surface evolution. Neuroimage 23:997–1012

    Article  PubMed  Google Scholar 

  38. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis I. Segmentation and surface reconstruction. Neuroimage 9:179–194

    Article  CAS  PubMed  Google Scholar 

  39. Kriegeskorte N, Goebel R (2001) An efficient algorithm for topologically correct segmentation of the cortical sheet in anatomical MR volumes. NeuroImage 14:329–346

    Article  CAS  PubMed  Google Scholar 

  40. Hutton C, De Vita E, Ashburner J, Deichmann R, Turner R (2008) Voxel-based cortical thickness measurements in MRI. Neuroimage 40(4):1701–1710

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hutton C, Draganski B, Ashburner J, Weiskopf N (2009) A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. Neuroimage 48(2):371–380

    Article  PubMed  PubMed Central  Google Scholar 

  42. Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RSR, Busa E, Morris JC, Dale AM, Fischl B (2004) Thinning of the cerebral cortex in aging. Cereb Cortex 14(7):721–730

    Article  PubMed  Google Scholar 

  43. Vachet C, Hazlett HC, Niethammer M, Oguz I, Cates J, Whitaker R, Piven J, Styner M (2011) Group-wise automatic mesh-based analysis of cortical thickness. In: Presented at the medical imaging 2011: image processing 7962(1):796227

  44. Shi F, Yap P-T, Wu G, Jia H, Gilmore JH, Lin W, Shen D (2011) Infant brain atlases from neonates to 1- and 2-year-olds. PLoS One 6(4):e18746

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Jia H, Yap PT, Shen D (2012) Iterative multi-atlas-based multi-image segmentation with tree-based registration. Neuroimage 59(1):422–430

    Article  PubMed  PubMed Central  Google Scholar 

  46. Wu G, Wang Q, Zhang D, Nie F, Huang H, Shen D (2014) A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med Image Anal 18(6):881–890

    Article  PubMed  PubMed Central  Google Scholar 

  47. Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Imag Anal 24(1):205–219

    Article  Google Scholar 

  48. Cabezas M, Oliver A, Lladó X, Freixenet J, Cuadra MB (2011) A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Programs Biomed 104(3):e158–e177. doi:10.1016/j.cmpb.2011.07.015

    Article  PubMed  Google Scholar 

  49. Yushkevich PA, Pluta J, Wang H, Ding SL, Xie L, Gertje E, Mancuso L, Kliot D, Das SR, Wolk DA (2014) Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Hum Brain Mapp 36(1):258–287

    Article  PubMed  PubMed Central  Google Scholar 

  50. Yang Z, Y C, Bogovic JA, Carass A, Jedynak BM, Ying SH, Prince JL (2015) Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease. Neuroimage doi:10.1016/j.neuroimage.2015.09.032. [Epub ahead of print]

  51. Bogovic JA, Bazin PL, Ying SH, Prince JL (2013) Automated segmentation of the cerebellar lobules using boundary specific classification and evolution. Inf Process Med Imaging 23:62–73

    Article  PubMed  PubMed Central  Google Scholar 

  52. Vachet C, Yvernault B, Bhatt K, Smith RG, Gerig G, Hazlett HC, Styner M (2012) Automatic corpus callosum segmentation using a deformable active Fourier contour model. Proc SPIE Int Soc Opt Eng 8317:831707. doi:10.1117/12.911504

    PubMed Central  Google Scholar 

  53. Helms G, Draganski B, Frackowiak R, Ashburner J, Weiskopf N (2009) Reliable segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps. Neuroimage 47:194–198

    Article  PubMed  PubMed Central  Google Scholar 

  54. Deoni SC, Rutt BK, Parrent AG, Peters TM (2007) Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 T. Neuroimage 34:117–126

    Article  PubMed  Google Scholar 

  55. Patenaude B, Smith SM, Kennedy D, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain. Neuroimage 56(3):907–922

    Article  PubMed  PubMed Central  Google Scholar 

  56. Krauth A, Blanc R, Poveda A, Jeanmonod D, Morel A, Székely G (2010) A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage 49(3):2053–2062

    Article  PubMed  Google Scholar 

  57. Hoult DI (2000) The principle of reciprocity in signal strength calculations—a mathematical guide. Concepts Magn Reson 14(4):173–187

    Article  Google Scholar 

  58. Volz S, Nöth U, Deichmann R (2012) Correction of systematic errors in quantitative proton density mapping. Magn Reson Med 68(1):74–85

    Article  PubMed  Google Scholar 

  59. Weiskopf N, Lutti A, Helms G, Novak M, Ashburner J, Hutton C (2011) Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT). Neuroimage 54(3):2116–2124

    Article  PubMed  PubMed Central  Google Scholar 

  60. Shin W, Geng X, Gu H, Zhan W, Zou Q, Yang Y (2010) Automated brain tissue segmentation based on fractional signal mapping from inversion recovery look-locker acquisition. Neuroimage 52:1347–1354

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ahlgren A, Wirestam R, Ståhlberg F, Knutsson L (2014) Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition. Magn Reson Mater Phy 27:551–565

    Article  Google Scholar 

  62. Draganski B, Ashburner J, Hutton C, Kherif F, Frackowiak RS, Helms G, Weiskopf N (2011) Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage 55(4):1423–1434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Càmara E, Bodammer N, Rodríguez-Fornells A, Tempelmann C (2007) Age-related water diffusion changes in human brain: a voxel-based approach. Neuroimage 34:1588–1599

    Article  PubMed  Google Scholar 

  64. Dick F, Tierney AT, Lutti A, Josephs O, Sereno MI, Weiskopf N (2012) In vivo functional and myeloarchitectonic mapping of human primary auditory areas. J Neurosci 32(46):16095–16105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Miller DH, Barkhof F, Frank JA, Parker GJM, Thompson AJ (2002) Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 125:1676–1692

    Article  PubMed  Google Scholar 

  66. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, De Stefano N (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17:479–489

    Article  PubMed  Google Scholar 

  67. Durand-Dubief F, Belaroussi B, Armspach JP, Dufour M, Roggerone S, Vukusic S, Hannoun S, Sappey-Marinier D, Confavreux C, Cotton F (2012) Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques. AJNR Am J Neuroradiol 33(10):1918–1924

    Article  CAS  PubMed  Google Scholar 

  68. Bernasconi A, Bernasconi N, Bernhardt BC, Schrader D (2011) Advances in MRI for ‘cryptogenic’ epilepsies. Nat Rev Neurol 7(2):99–108

    Article  PubMed  Google Scholar 

  69. Martin P, Bender B, Focke NK (2015) Post-processing of structural MRI for individualized diagnostics. Quant Imaging Med Surg 5(2):188–203

    PubMed  PubMed Central  Google Scholar 

  70. Teipel SJ, Grothe M, Lista S, Toschi N, Garaci FG, Hampel H (2013) Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease. Med Clin North Am 97(3):399–424

    Article  PubMed  Google Scholar 

  71. Braskie MN, Thompson PM (2014) A focus on structural brain imaging in the Alzheimer’s disease neuroimaging initiativ. Biol Psychiatry 75(7):527–533

    Article  PubMed  PubMed Central  Google Scholar 

  72. Jack CR Jr, Barkhof F, Bernstein MA, Cantillon M, Cole PE, DeCarli C, Dubois B, Duchesne S, Fox NC, Frisoni GB, Hampel H, Hill DLG, Johnson K, Mangin J-F, Scheltens P, Schwarz AJ, Sperling R, Suhy J, Thompson PM, Weiner M, Foster NL (2011) Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criteria for Alzheimer’s disease. Alzheimers Dement 7(4):474–485

    Article  PubMed  PubMed Central  Google Scholar 

  73. Horn A, Kühn AA (2014) Lead-DBS: a toolbox for deep brain stimulation electrode localizations and visualizations. Neuroimage 107:127–135

    Article  PubMed  Google Scholar 

  74. Shiee N, Bazin P-L, Ozturk A, Reich DS, Calabresi PA, Pham DL (2010) A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49:1524–1535

    Article  PubMed  PubMed Central  Google Scholar 

  75. Prastawa M, Gerig G (2008) Brain Lesion Segmentation through physical model estimation. Int Symp Vis Comput (ISVC) Lect Notes Comput Sci (LNCS) 5358:562–571

    Article  Google Scholar 

  76. Lao Z, Shen D, Liu D, Jawad AF, Melhem ER, Launer LJ, Bryan NR, Davatzikos C (2008) Computer-assisted segmentation of white matter lesions in 3D MR images using pattern recognition. Acad Radiol 15(3):300–313

    Article  PubMed  PubMed Central  Google Scholar 

  77. Ithapu V, Singh V, Lindner C, Austin BP, Hinrichs C, Carlsson CM, Bendlin BB, Johnson SC (2014) Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies. Hum Brain Mapp. doi:10.1002/hbm.22472

    PubMed  PubMed Central  Google Scholar 

  78. García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL (2013) Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal 17(1):1–18

    Article  PubMed  Google Scholar 

  79. Moon N, Bullitt E, van Leemput K, Gerig G (2002) Automatic brain and tumor segmentation. In: Proceedings of MICCAI ‘02, Springer LNCS 2488, 09/2002

  80. Bauer S, Wiest R, Nolte LP, Reyes M (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):R97–R129

    Article  PubMed  Google Scholar 

  81. Wang L, Shi F, Yap P-T, Lin W, Gilmore JH, Shen D (2013) Longitudinally guided level sets for consistent tissue segmentation of neonates. Hum Brain Mapp 34:956–972

    Article  PubMed  Google Scholar 

  82. Wang B, Prastawa M, Irimia A, Chambers MC, Sadeghi N, Vespa PM, van Horn JD, Gerig G (2013) Analyzing imaging biomarkers for traumatic brain injury using 4D modeling of longitudinal MRI. Proc IEEE Int Symp Biomed Imaging 2013:1392–1395

    PubMed  PubMed Central  Google Scholar 

  83. Shiee N, Bazin P-L, Zackowski KM, Farrell SK, Harrison DM, Newsome SD, Ratchford JN, Caffo BS, Calabresi PA, Pham DL, Reich DS (2012) Revisiting brain atrophy and its relationship to disability in multiple sclerosis. PLoS One 7(5):e37049. doi:10.1371/journal.pone.0037049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical Radiation Physics, Lund University Hospital, Barngatan 2B, 221 85, Lund, Sweden

    Gunther Helms

Authors
  1. Gunther Helms
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gunther Helms.

Ethics declarations

All procedures performed in studies involving human participants were accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interest

The author has no conflicts of interest to declare.

Funding

The author is supported by the Swedish Research Council.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Helms, G. Segmentation of human brain using structural MRI. Magn Reson Mater Phy 29, 111–124 (2016). https://doi.org/10.1007/s10334-015-0518-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10334-015-0518-z

Keywords

Search

Navigation