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



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.


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.


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.


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.


MRI Segmentation Hippocampus Thalamus MP2RAGE Diffusion-weighted imaging 



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.


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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

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