Skip to main content
SpringerLink
Log in

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

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

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

Methods

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).

Results

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.

Conclusion

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.

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

Similar content being viewed by others

Abbreviations

GM:

Gray matter

WM:

White matter

CSF:

Cerebrospinal fluid

FLAIR:

Fluid-attenuated inversion recovery

AAL:

Automatic Anatomical Labeling

3D:

Three-dimensional

TI:

Inversion time

TR:

Repetition time

TE:

Echo time

FSL:

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

FAST:

FMRIB’s Automated Segmentation Tool

BET:

Brain extraction

AFNI:

Analyses of functional images

MNI:

Montreal Neurological Institute

FLIRT:

FMRIB’s linear image registration tool

FNIRT:

FMRIB’s nonlinear image registration tool

ICV:

Intracranial volume

r :

Pearson correlation

R:

Right

L:

Left

References

  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 19:1523–1536

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 277:206–220

    Article  PubMed  Google Scholar 

  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 233:299–305

    Article  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.neuroimage.2017.02.016

  5. Gatidis S, Heber SD, Storz C, Bamberg F (2016) Population-based imaging biobanks as source of big data. Radiol Med 122:430–436. https://doi.org/10.1007/s11547-016-0684-8

    Article  PubMed  Google Scholar 

  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 9:379

    Article  PubMed  PubMed Central  Google Scholar 

  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 130:157–166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Utter AA, Basso MA (2008) The basal ganglia: an overview of circuits and function. Neurosci Biobehav Rev 32:333–342

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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 15:273–289

    Article  CAS  PubMed  Google Scholar 

  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 17:825–841

    Article  PubMed  Google Scholar 

  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:e74935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

  14. Fischl B (2012) FreeSurfer. Neuroimage 62:774–781

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195–207

    Article  CAS  PubMed  Google Scholar 

  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 34:11199–11211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 36:1450–1456

    Article  CAS  PubMed  Google Scholar 

  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 25:191–199

    Article  PubMed  Google Scholar 

  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 9:238

    Article  PubMed  PubMed Central  Google Scholar 

  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 125:479–497

    Article  PubMed  PubMed Central  Google Scholar 

  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 139:324–336

    Article  PubMed  PubMed Central  Google Scholar 

  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 231:227–235

    Article  PubMed  Google Scholar 

  23. Despotovic I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015:450341

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Author notes

  1. EB and DK contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Ludwig-Maximilians University Munich, Munich, Germany

    Ebba Beller, Daniel Keeser, Antonia Wehn, Temmuz Karali, Boris Papazov, Franziska Schoeppe, Giovanna Negrao de Figueiredo, Birgit Ertl-Wagner & Sophia Stoecklein

  2. Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Universitätsmedizin Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany

    Ebba Beller

  3. Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany

    Daniel Keeser, Berend Malchow, Temmuz Karali, Andrea Schmitt, Irina Papazova & Boris Papazov

  4. Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo, Rua Dr. Ovidio Pires de Campos 785, São Paulo, SP, 05453-010, Brazil

    Andrea Schmitt

  5. Department of Medical Imaging, The Hospital for Sick Children, University of Toronto, Toronto, Canada

    Birgit Ertl-Wagner

Authors
  1. Ebba Beller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Keeser
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonia Wehn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Berend Malchow
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Temmuz Karali
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrea Schmitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Irina Papazova
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Boris Papazov
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Franziska Schoeppe
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Giovanna Negrao de Figueiredo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Birgit Ertl-Wagner
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sophia Stoecklein
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ebba Beller.

Ethics declarations

Funding

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.

Electronic supplementary material

Supplementary Table 1a

(DOCX 104 kb)

Supplementary Table 1b

(DOCX 131 kb)

Supplementary Table 2

(DOCX 40 kb)

Supplementary Table 3

(DOCX 96 kb)

ESM 1

(PDF 156 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beller, E., Keeser, D., Wehn, A. et al. T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions—an MRI evaluation study. Neuroradiology 61, 129–136 (2019). https://doi.org/10.1007/s00234-018-2121-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-018-2121-2

Keywords

Search

Navigation