Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Magnetic Resonance Materials in Physics, Biology and Medicine
Published in: Magnetic Resonance Materials in Physics, Biology and Medicine 2/2016

01-04-2016 | Review Article

Segmentation of human brain using structural MRI

Author: Gunther Helms

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine | Issue 2/2016

Login to get access

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.
Literature
1.
Lim KO, Pfefferbaum A (1989) Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. J Comput Assist Tomogr 13:588–593CrossRefPubMed
2.
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 18:5–17CrossRef
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):1323–1341CrossRefPubMedPubMedCentral
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):903–921CrossRefPubMedPubMedCentral
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):482–495CrossRefPubMed
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):265–274CrossRefPubMed
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):759–767CrossRefPubMed
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):71–79CrossRefPubMed
10.
Hallgren B, Sourander P (1958) The effect of age on the non-haemin iron in the human brain. J Neurochem 3(1):41–51CrossRefPubMed
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):790–795CrossRefPubMed
12.
Deichmann R, Good CD, Josephs O, Ashburner J, Turner R (2000) Optimization of 3-D MP-RAGE sequences for structural brain imaging. Neuroimage 12:112–127CrossRefPubMed
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 21:757–767CrossRefPubMed
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):685–691CrossRefPubMedPubMedCentral
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):84–93CrossRefPubMed
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 17:87–97CrossRef
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):1310–1320CrossRef
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):45–57CrossRefPubMed
19.
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 30:1617–1634CrossRefPubMed
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):1060–1075CrossRefPubMed
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):1982–1992CrossRefPubMedPubMedCentral
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):1235–1241CrossRefPubMed
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–S186CrossRefPubMed
25.
Ashburner J, Friston K (1997) Multimodal image coregistration and partitioning—a unified framework. Neuroimage 6(3):209–217CrossRefPubMed
26.
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 263–274CrossRef
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):897–908CrossRefPubMed
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):311–312CrossRefPubMed
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):1164–1170CrossRefPubMed
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):684–694CrossRef
32.
33.
34.
Brownstein KR, Tarr CE (1977) Spin-lattice relaxation in a system governed by diffusion. J Magn Reson 26:17–24
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):11050–11055CrossRefPubMedPubMedCentral
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):210–221CrossRefPubMed
37.
Han X, Pham D, Tosun D, Rettmann M, Xu C, Prince J (2004) CRUISE: cortical reconstruction using implicit surface evolution. Neuroimage 23:997–1012CrossRefPubMed
38.
Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis I. Segmentation and surface reconstruction. Neuroimage 9:179–194CrossRefPubMed
39.
Kriegeskorte N, Goebel R (2001) An efficient algorithm for topologically correct segmentation of the cortical sheet in anatomical MR volumes. NeuroImage 14:329–346CrossRefPubMed
40.
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):371–380CrossRefPubMedPubMedCentral
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):721–730CrossRefPubMed
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.
45.
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):881–890CrossRefPubMedPubMedCentral
47.
Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Imag Anal 24(1):205–219CrossRef
48.
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):258–287CrossRefPubMedPubMedCentral
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 23:62–73CrossRefPubMedPubMedCentral
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 PubMedCentral
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 47:194–198CrossRefPubMedPubMedCentral
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 34:117–126CrossRefPubMed
55.
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):2053–2062CrossRefPubMed
57.
Hoult DI (2000) The principle of reciprocity in signal strength calculations—a mathematical guide. Concepts Magn Reson 14(4):173–187CrossRef
58.
Volz S, Nöth U, Deichmann R (2012) Correction of systematic errors in quantitative proton density mapping. Magn Reson Med 68(1):74–85CrossRefPubMed
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):2116–2124CrossRefPubMedPubMedCentral
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 52:1347–1354CrossRefPubMedPubMedCentral
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 27:551–565CrossRef
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):1423–1434CrossRefPubMedPubMedCentral
63.
Càmara E, Bodammer N, Rodríguez-Fornells A, Tempelmann C (2007) Age-related water diffusion changes in human brain: a voxel-based approach. Neuroimage 34:1588–1599CrossRefPubMed
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):16095–16105CrossRefPubMedPubMedCentral
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 125:1676–1692CrossRefPubMed
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 17:479–489CrossRefPubMed
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):1918–1924CrossRefPubMed
68.
Bernasconi A, Bernasconi N, Bernhardt BC, Schrader D (2011) Advances in MRI for ‘cryptogenic’ epilepsies. Nat Rev Neurol 7(2):99–108CrossRefPubMed
69.
Martin P, Bender B, Focke NK (2015) Post-processing of structural MRI for individualized diagnostics. Quant Imaging Med Surg 5(2):188–203PubMedPubMedCentral
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):399–424CrossRefPubMed
71.
Braskie MN, Thompson PM (2014) A focus on structural brain imaging in the Alzheimer’s disease neuroimaging initiativ. Biol Psychiatry 75(7):527–533CrossRefPubMedPubMedCentral
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):474–485CrossRefPubMedPubMedCentral
73.
Horn A, Kühn AA (2014) Lead-DBS: a toolbox for deep brain stimulation electrode localizations and visualizations. Neuroimage 107:127–135CrossRefPubMed
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 49:1524–1535CrossRefPubMedPubMedCentral
75.
Prastawa M, Gerig G (2008) Brain Lesion Segmentation through physical model estimation. Int Symp Vis Comput (ISVC) Lect Notes Comput Sci (LNCS) 5358:562–571CrossRef
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):300–313CrossRefPubMedPubMedCentral
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 PubMedPubMedCentral
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):1–18CrossRefPubMed
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–R129CrossRefPubMed
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 34:956–972CrossRefPubMed
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 2013:1392–1395PubMedPubMedCentral
83.
Metadata
Title
Segmentation of human brain using structural MRI
Author
Gunther Helms
Publication date
01-04-2016
Publisher
Springer Berlin Heidelberg
Published in
Magnetic Resonance Materials in Physics, Biology and Medicine / Issue 2/2016
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI
https://doi.org/10.1007/s10334-015-0518-z

Other articles of this Issue 2/2016

Magnetic Resonance Materials in Physics, Biology and Medicine 2/2016 Go to the issue

Review Article

A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

Review Article

Segmentation of the human spinal cord

Research Article

Segmentation and characterization of interscapular brown adipose tissue in rats by multi-parametric magnetic resonance imaging

Research Article

Multi-atlas-based fully automatic segmentation of individual muscles in rat leg

Review Article

Segmentation of joint and musculoskeletal tissue in the study of arthritis

Review Article

Segmentation and quantification of adipose tissue by magnetic resonance imaging