Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

  • Erhard Næss-Schmidt
  • Anna Tietze
  • Jakob Udby Blicher
  • Mikkel Petersen
  • Irene K. Mikkelsen
  • Pierrick Coupé
  • José V. Manjón
  • Simon Fristed Eskildsen
Original Article

Abstract

Purpose

In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques.

Methods

Four publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects.

Results

Compared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library (\(M=0.913\), \(\hbox {SD}=0.014\)) followed by volBrain using an external library (\(M=0.868\), \(\hbox {SD}=0.024\)), FSL (\({M}=0.806\), \(\mathrm{SD}=0.034\)), FreeSurfer (\({M}=0.798\), \(\mathrm{SD}=0.049\)) and SPM (\({M}=0.787\), \(\mathrm{SD}=0.031\)). The same order is found for hippocampus with volBrain local (\({M}=0.892\), \(\mathrm{SD}=0.016\)), volBrain external (\({M}=0.859\), \(\mathrm{SD}=0.014\)), FSL (\({M}=0.808\), \(\mathrm{SD}=0.017\)), FreeSurfer (\({M}=0.771\), \(\mathrm{SD}=0.023\)) and SPM (\({M}=0.735\), \(\mathrm{SD}=0.038\)). For diffusivity measurements, volBrain provides values closest to those obtained from manual segmentations. volBrain is the only method where FA values do not differ significantly from manual segmentation of the thalamus.

Conclusions

Overall we find that volBrain is superior in thalamus and hippocampus segmentation compared to FSL, FreeSurfer and SPM. Furthermore, the choice of segmentation technique and training library affects quantitative results from diffusivity measures in thalamus and hippocampus.

Keywords

MRI Segmentation Hippocampus Thalamus MP2RAGE Diffusion-weighted imaging 

Notes

Acknowledgments

This work was funded in part by MINDLab UNIK initiative at Aarhus University, funded by the Danish Ministry of Science, Technology and Innovation, Grant Agreement Number 09-065250, partly by the Spanish grant TIN2013-43457-R from the Ministerio de Economia competitividad and with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02) by funding HL-DTI grant, Cluster of excellence CPU, LaBEX TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi ImagIn”.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the study 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. The study was a retrospective study. For this type of study, formal consent is not required.

References

  1. 1.
    Mulder ER, de Jong RA, Knol DL, van Schijndel RA, Cover KS, Visser PJ, Barkhof F, Vrenken H (2014) Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST. Neuroimage 92:169–181CrossRefPubMedGoogle Scholar
  2. 2.
    Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–126CrossRefPubMedGoogle Scholar
  3. 3.
    Rohlfing T, Brandt R, Menzel R, Maurer CR (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21(4):1428–1442CrossRefPubMedGoogle Scholar
  4. 4.
    Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3):726–738CrossRefPubMedGoogle Scholar
  5. 5.
    Coupé P, Manjón JV, Fonov V, Pruessner J, Robles M, Collins DL (2011) Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54(2):940–954CrossRefPubMedGoogle Scholar
  6. 6.
    Tong T, Wolz R, Coupé P, Hajnal JV, Rueckert D (2013) Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. Neuroimage 76:11–23CrossRefPubMedGoogle Scholar
  7. 7.
    Eskildsen SF, Coupé P, Fonov V, Manjón JV, Leung KK, Guizard N, Wassef SN, Østergaard LR, Collins DL (2012) BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 59(3):2362–2373CrossRefPubMedGoogle Scholar
  8. 8.
    Falangola MF, Jensen JH, Tabesh A, Hu C, Deardorff RL, Babb JS, Ferris S, Helpern JA (2013) Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease. Magn Reson Imaging 31(6):840–846CrossRefPubMedGoogle Scholar
  9. 9.
    Mitchell AS, Sherman SM, Sommer MA, Mair RG, Vertes RP, Chudasama Y (2014) Advances in understanding mechanisms of thalamic relays in cognition and behavior. J Neurosci 34(46):15340–15346CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Vestergaard-Poulsen P, Wegener G, Hansen B, Bjarkam CR, Blackband SJ, Nielsen NC, Jespersen SN (2011) Diffusion-weighted MRI and quantitative biophysical modeling of hippocampal neurite loss in chronic stress. PLoS ONE 6(7):e20653CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Granziera C, Daducci A, Romascano D, Roche A, Helms G, Krueger G, Hadjikhani N (2014) Structural abnormalities in the thalamus of migraineurs with aura: a multiparametric study at 3 T. Hum Brain Mapp 35(4):1461–1468CrossRefPubMedGoogle Scholar
  12. 12.
    Coupé P, Eskildsen SF, Manjón JV, Fonov VS, Collins DL (2012) Simultaneous segmentation and grading of anatomical structures for patient’s classification: application to Alzheimer’s disease. Neuroimage 59(4):3736–3747CrossRefPubMedGoogle Scholar
  13. 13.
    Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PFF, Gruetter R (2010) MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49(2):1271–1281CrossRefPubMedGoogle Scholar
  14. 14.
    Fujimoto K, Polimeni JR, van der Kouwe AJW, Reuter M, Kober T, Benner T, Fischl B, Wald LL (2014) Quantitative comparison of cortical surface reconstructions from MP2RAGE and multi-echo MPRAGE data at 3 and 7 T. Neuroimage 90:60–73CrossRefPubMedGoogle Scholar
  15. 15.
    Dudo RO, Hart PE, Stork D (2001) Pattern classification, 2nd edn. Wiley, HobokenGoogle Scholar
  16. 16.
    Leemans A, Jeurissen B, Sijbers J, Jones D (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: Proceedings 17th scientific meeting, international society for magnetic resonance in medicine, vol 17, no 2, p 3537Google Scholar
  17. 17.
    Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128CrossRefPubMedGoogle Scholar
  18. 18.
    Power BD, Wilkes FA, Hunter-Dickson M, van Westen D, Santillo AF, Walterfang M, Nilsson C, Velakoulis D, Looi JCL (2015) Validation of a protocol for manual segmentation of the thalamus on magnetic resonance imaging scans. Psychiatry Res 232(1):98–105CrossRefPubMedGoogle Scholar
  19. 19.
    Boccardi M, Bocchetta M, Apostolova LG, Barnes J, Bartzokis G, Corbetta G,DeCarliC, deToledo-Morrell L, Firbank M, Ganzola R, Gerritsen L, Henneman W, Killiany RJ, Malykhin N, Pasqualetti P, Pruessner JC, Redolfi A, Robitaille N, Soininen H, Tolomeo D, Wang L, Watson C, Wolf H, Duvernoy H, Duchesne S, Jack CR, Frisoni GB (2014) Delphi definition of the EADC-ADNI harmonized protocol for hippocampal segmentation on magnetic resonance. Alzheimers Dement 11(2):126–138Google Scholar
  20. 20.
    Manjón JV, Coupé P (2015) volBrain: an online MRI brain volumetry system. Hum Brain Mapp 15:2015Google Scholar
  21. 21.
    Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3):907–922CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, Van Der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355CrossRefPubMedGoogle Scholar
  23. 23.
    Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851CrossRefPubMedGoogle Scholar
  24. 24.
    Frisoni GB, Jack CR, Bocchetta M, Bauer C, Frederiksen KS, Liu Y, Preboske G, Swihart T, Blair M, Cavedo E, Grothe MJ, Lanfredi M, Martinez O, Nishikawa M, Portegies M, Stoub T, Ward C, Apostolova LG, Ganzola R, Wolf D, Barkhof F, Bartzokis G, DeCarli C, Csernansky JG, Detoledo-Morrell L, Geerlings MI, Kaye J, Killiany RJ, Lehericy S, Matsuda H, O’Brien J, Silbert LC, Scheltens P, Soininen H, Teipel S, Waldemar G, Fellgiebel A, Barnes J, Firbank M, Gerritsen L, Henneman W, Malykhin N, Pruessner JC, Wang L, Watsonl C, Wolf H, Deleon M, Pantel J, Ferrari C, Bosco P, Pasqualetti P, Duchesne S, Duvernoy H, Boccardi M, Albert MS, Bennet D, Camicioli R, Collins DL, Dubois B, Hampel H, Denheijer T, Hock C, Jagust W, Launer L, Maller JJ, Mueller S, Sachdev P, Simmons A, Thompson PM, Visser PJ, Wahlund LO, Weiner MW, Winblad B (2015) The EADC-ADNI harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer’s Dement 11(2):111–125CrossRefGoogle Scholar
  25. 25.
    Næss-Schmidt ET, Tietze A, Mikkelsen IK, Petersen M, Blicher JU, Coupé P, Manjón JV, Eskildsen SF (2015) Patch-based segmentation from MP2RAGE images: comparison to conventional techniques. In: Wu G, Coupé P, Zhan Y, Munsell B, Rueckert D (eds) First international workshop, patch-techniques in medical imaging. Lecture notes in computer science, held in conjunction with MICCAI 2015, vol 9467. Munich, Germany, pp.180–187Google Scholar
  26. 26.
    Barbagallo G, Nicoletti G, Cherubini A, Trotta M, Tallarico T, Chiriaco C, Nisticò R, Salvino D, Bono F, Valentino P, Quattrone A (2014) Diffusion tensor MRI changes in gray structures of the frontal-subcortical circuits in amyotrophic lateral sclerosis. Neurol Sci 35(6):911–918CrossRefPubMedGoogle Scholar
  27. 27.
    Okubo G, Okada T, Yamamoto A, KanagakiM, Fushimi Y, Okada T, Murata K, Togashi K (2015) MP2RAGE for deep gray matter measurement of the brain: a comparative study with MPRAGE. J Magn Reson Imaging 43(1):55–62Google Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  • Erhard Næss-Schmidt
    • 1
    • 6
  • Anna Tietze
    • 2
    • 3
  • Jakob Udby Blicher
    • 2
  • Mikkel Petersen
    • 2
  • Irene K. Mikkelsen
    • 2
  • Pierrick Coupé
    • 4
  • José V. Manjón
    • 5
  • Simon Fristed Eskildsen
    • 2
  1. 1.Hammel Neurorehabilitation Centre and University Research ClinicAarhus UniversityHammelDenmark
  2. 2.Center of Functionally Integrative Neuroscience and MINDLabAarhus UniversityAarhusDenmark
  3. 3.Department of NeuroradiologyAarhus University HospitalAarhusDenmark
  4. 4.Laboratoire Bordelais de Recherche en InformatiqueUnité Mixte de Recherche CNRS (UMR 5800)Talence cedexFrance
  5. 5.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universitat Politècnica de ValènciaValenciaSpain
  6. 6.Hammel Neurorehabilitation Centre and University Research ClinicHammelDenmark

Personalised recommendations