, Volume 61, Issue 2, pp 129–136 | Cite as

T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions—an MRI evaluation study

  • Ebba BellerEmail author
  • Daniel Keeser
  • Antonia Wehn
  • Berend Malchow
  • Temmuz Karali
  • Andrea Schmitt
  • Irina Papazova
  • Boris Papazov
  • Franziska Schoeppe
  • Giovanna Negrao de Figueiredo
  • Birgit Ertl-Wagner
  • Sophia Stoecklein
Diagnostic Neuroradiology



Development of a warp-based automated brain segmentation approach of 3D fluid-attenuated inversion recovery (FLAIR) images and comparison to 3D T1-based segmentation.


3D FLAIR and 3D T1-weighted sequences of 30 healthy subjects (mean age 29.9 ± 8.3 years, 8 female) were acquired on the same 3T MR scanner. Warp-based segmentation was applied for volumetry of total gray matter (GM), white matter (WM), and 116 atlas regions. Segmentation results of both sequences were compared using Pearson correlation (r).


Correlation of GM segmentation results based on FLAIR and T1 was overall good for cortical structures (mean r across all cortical structures = 0.76). Comparatively weaker results were found in the occipital lobe (r = 0.77), central region (mean r = 0.58), basal ganglia (mean r = 0.59), thalamus (r = 0.30), and cerebellum (r = 0.73). FLAIR segmentation underestimated volume of the central region compared to T1, but showed a better anatomic concordance with the occipital lobe on visual review and subcortical structures, when also compared to manual segmentation. Visual analysis of FLAIR-based WM segmentation revealed frequent misclassification of regions of high signal intensity as GM.


Warp-based FLAIR segmentation yields comparable results to T1 segmentation for most cortical GM structures and may provide anatomically more congruent segmentation of subcortical GM structures. Selected cortical regions, especially the central region and total WM, seem to be underestimated on FLAIR segmentation.


Magnetic resonance imaging Brain Neuroanatomy Cohort studies 



Gray matter


White matter


Cerebrospinal fluid


Fluid-attenuated inversion recovery


Automatic Anatomical Labeling




Inversion time


Repetition time


Echo time


Functional magnetic resonance imaging of the brain (FMRIB) software library


FMRIB’s Automated Segmentation Tool


Brain extraction


Analyses of functional images


Montreal Neurological Institute


FMRIB’s linear image registration tool


FMRIB’s nonlinear image registration tool


Intracranial volume


Pearson correlation






Compliance with ethical standards


No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

234_2018_2121_MOESM1_ESM.docx (104 kb)
Supplementary Table 1a (DOCX 104 kb)
234_2018_2121_MOESM2_ESM.docx (132 kb)
Supplementary Table 1b (DOCX 131 kb)
234_2018_2121_MOESM3_ESM.docx (40 kb)
Supplementary Table 2 (DOCX 40 kb)
234_2018_2121_MOESM4_ESM.docx (97 kb)
Supplementary Table 3 (DOCX 96 kb)
234_2018_2121_MOESM5_ESM.pdf (157 kb)
ESM 1 (PDF 156 kb)


  1. 1.
    Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JLR, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19:1523–1536CrossRefGoogle Scholar
  2. 2.
    Bamberg F, Kauczor HU, Weckbach S, Schlett CL, Forsting M, Ladd SC, Greiser KH, Weber MA, Schulz-Menger J, Niendorf T, Pischon T, Caspers S, Amunts K, Berger K, Bülow R, Hosten N, Hegenscheid K, Kröncke T, Linseisen J, Günther M, Hirsch JG, Köhn A, Hendel T, Wichmann HE, Schmidt B, Jöckel KH, Hoffmann W, Kaaks R, Reiser MF, Völzke H, For the German National Cohort MRI Study Investigators (2015) Whole-body MR imaging in the German National Cohort: rationale, design, and technical background. Radiology 277:206–220CrossRefGoogle Scholar
  3. 3.
    Fellhauer I, Zollner FG, Schroder J et al (2015) Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer’s dementia or mild cognitive impairment. Psychiatry Res 233:299–305CrossRefGoogle Scholar
  4. 4.
    Lindig T, Kotikalapudi R, Schweikardt D et al (2017) Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing. Neuroimage.
  5. 5.
    Gatidis S, Heber SD, Storz C, Bamberg F (2016) Population-based imaging biobanks as source of big data. Radiol Med 122:430–436. CrossRefGoogle Scholar
  6. 6.
    McCarthy CS, Ramprashad A, Thompson C, Botti JA, Coman IL, Kates WR (2015) A comparison of FreeSurfer-generated data with and without manual intervention. Front Neurosci 9:379CrossRefGoogle Scholar
  7. 7.
    Lorio S, Fresard S, Adaszewski S, Kherif F, Chowdhury R, Frackowiak RS, Ashburner J, Helms G, Weiskopf N, Lutti A, Draganski B (2016) New tissue priors for improved automated classification of subcortical brain structures on MRI. Neuroimage 130:157–166CrossRefGoogle Scholar
  8. 8.
    Utter AA, Basso MA (2008) The basal ganglia: an overview of circuits and function. Neurosci Biobehav Rev 32:333–342CrossRefGoogle Scholar
  9. 9.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155CrossRefGoogle Scholar
  10. 10.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289CrossRefGoogle Scholar
  11. 11.
    Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841CrossRefGoogle Scholar
  12. 12.
    Park B, Ko JH, Lee JD, Park HJ (2013) Evaluation of node-inhomogeneity effects on the functional brain network properties using an anatomy-constrained hierarchical brain parcellation. PLoS One 8:e74935CrossRefGoogle Scholar
  13. 13.
    Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194CrossRefGoogle Scholar
  14. 14.
    Fischl B (2012) FreeSurfer. Neuroimage 62:774–781CrossRefGoogle Scholar
  15. 15.
    Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195–207CrossRefGoogle Scholar
  16. 16.
    Qureshi AY, Mueller S, Snyder AZ, Mukherjee P, Berman JI, Roberts TPL, Nagarajan SS, Spiro JE, Chung WK, Sherr EH, Buckner RL, on behalf of the Simons VIP Consortium (2014) Opposing brain differences in 16p11.2 deletion and duplication carriers. J Neurosci 34:11199–11211CrossRefGoogle Scholar
  17. 17.
    Hansen TI, Brezova V, Eikenes L, Haberg A, Vangberg TR (2015) How does the accuracy of intracranial volume measurements affect normalized brain volumes? Sample size estimates based on 966 subjects from the HUNT MRI cohort. AJNR Am J Neuroradiol 36:1450–1456CrossRefGoogle Scholar
  18. 18.
    Dell’Oglio E, Ceccarelli A, Glanz BI et al (2015) Quantification of global cerebral atrophy in multiple sclerosis from 3T MRI using SPM: the role of misclassification errors. J Neuroimaging 25:191–199CrossRefGoogle Scholar
  19. 19.
    Pintzka CW, Hansen TI, Evensmoen HR, Haberg AK (2015) Marked effects of intracranial volume correction methods on sex differences in neuroanatomical structures: a HUNT MRI study. Front Neurosci 9:238CrossRefGoogle Scholar
  20. 20.
    Visser E, Keuken MC, Douaud G, Gaura V, Bachoud-Levi AC, Remy P, Forstmann BU, Jenkinson M (2016) Automatic segmentation of the striatum and globus pallidus using MIST: multimodal image segmentation tool. Neuroimage 125:479–497CrossRefGoogle Scholar
  21. 21.
    Visser E, Keuken MC, Forstmann BU, Jenkinson M (2016) Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7T data at young and old age. Neuroimage 139:324–336CrossRefGoogle Scholar
  22. 22.
    Nordenskjold R, Malmberg F, Larsson EM et al (2015) Intracranial volume normalization methods: considerations when investigating gender differences in regional brain volume. Psychiatry Res 231:227–235CrossRefGoogle Scholar
  23. 23.
    Despotovic I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015:450341CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ebba Beller
    • 1
    • 2
    Email author
  • Daniel Keeser
    • 1
    • 3
  • Antonia Wehn
    • 1
  • Berend Malchow
    • 3
  • Temmuz Karali
    • 1
    • 3
  • Andrea Schmitt
    • 3
    • 4
  • Irina Papazova
    • 3
  • Boris Papazov
    • 1
    • 3
  • Franziska Schoeppe
    • 1
  • Giovanna Negrao de Figueiredo
    • 1
  • Birgit Ertl-Wagner
    • 1
    • 5
  • Sophia Stoecklein
    • 1
  1. 1.Department of RadiologyLudwig-Maximilians University MunichMunichGermany
  2. 2.Institut für Diagnostische und Interventionelle Radiologie, Kinder- und NeuroradiologieUniversitätsmedizin RostockRostockGermany
  3. 3.Department of Psychiatry and PsychotherapyUniversity Hospital, LMU MunichMunichGermany
  4. 4.Laboratory of Neuroscience (LIM27), Institute of PsychiatryUniversity of Sao PauloSão PauloBrazil
  5. 5.Department of Medical Imaging, The Hospital for Sick ChildrenUniversity of TorontoTorontoCanada

Personalised recommendations