Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches

  • Arnaud Le TroterEmail author
  • Alexandre Fouré
  • Maxime Guye
  • Sylviane Confort-Gouny
  • Jean-Pierre Mattei
  • Julien Gondin
  • Emmanuelle Salort-Campana
  • David Bendahan
Research Article



Atlas-based segmentation is a powerful method for automatic structural segmentation of several sub-structures in many organs. However, such an approach has been very scarcely used in the context of muscle segmentation, and so far no study has assessed such a method for the automatic delineation of individual muscles of the quadriceps femoris (QF). In the present study, we have evaluated a fully automated multi-atlas method and a semi-automated single-atlas method for the segmentation and volume quantification of the four muscles of the QF and for the QF as a whole.

Subjects and methods

The study was conducted in 32 young healthy males, using high-resolution magnetic resonance images (MRI) of the thigh. The multi-atlas-based segmentation method was conducted in 25 subjects. Different non-linear registration approaches based on free-form deformable (FFD) and symmetric diffeomorphic normalization algorithms (SyN) were assessed. Optimal parameters of two fusion methods, i.e., STAPLE and STEPS, were determined on the basis of the highest Dice similarity index (DSI) considering manual segmentation (MSeg) as the ground truth. Validation and reproducibility of this pipeline were determined using another MRI dataset recorded in seven healthy male subjects on the basis of additional metrics such as the muscle volume similarity values, intraclass coefficient, and coefficient of variation. Both non-linear registration methods (FFD and SyN) were also evaluated as part of a single-atlas strategy in order to assess longitudinal muscle volume measurements. The multi- and the single-atlas approaches were compared for the segmentation and the volume quantification of the four muscles of the QF and for the QF as a whole.


Considering each muscle of the QF, the DSI of the multi-atlas-based approach was high 0.87 ± 0.11 and the best results were obtained with the combination of two deformation fields resulting from the SyN registration method and the STEPS fusion algorithm. The optimal variables for FFD and SyN registration methods were four templates and a kernel standard deviation ranging between 5 and 8. The segmentation process using a single-atlas-based method was more robust with DSI values higher than 0.9. From the vantage of muscle volume measurements, the multi-atlas-based strategy provided acceptable results regarding the QF muscle as a whole but highly variable results regarding individual muscle. On the contrary, the performance of the single-atlas-based pipeline for individual muscles was highly comparable to the MSeg, thereby indicating that this method would be adequate for longitudinal tracking of muscle volume changes in healthy subjects.


In the present study, we demonstrated that both multi-atlas and single-atlas approaches were relevant for the segmentation of individual muscles of the QF in healthy subjects. Considering muscle volume measurements, the single-atlas method provided promising perspectives regarding longitudinal quantification of individual muscle volumes.


MRI Multi-atlas-based segmentation Quadriceps femoris muscle Non-linear registration Fusion Individual muscle volume measurements 



This study was supported by the Centre National de la Recherche Scientifique (CNRS, UMR 7339). The authors thank the subjects who participated in the present study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in 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

10334_2016_535_MOESM1_ESM.docx (22 kb)
Supplementary material 1 (DOCX 23 kb)


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Copyright information

© ESMRMB 2016

Authors and Affiliations

  • Arnaud Le Troter
    • 1
    • 2
    Email author
  • Alexandre Fouré
    • 1
    • 2
  • Maxime Guye
    • 1
    • 2
  • Sylviane Confort-Gouny
    • 1
    • 2
  • Jean-Pierre Mattei
    • 1
    • 3
  • Julien Gondin
    • 1
    • 2
  • Emmanuelle Salort-Campana
    • 4
  • David Bendahan
    • 1
    • 2
  1. 1.Aix Marseille Université, CNRSCRMBM UMR 7339MarseilleFrance
  2. 2.APHM, CHU TimonePôle imagerie médicale, CEMEREMMarseilleFrance
  3. 3.APHM, CHU Sainte-MargueriteDépartement de RhumatologieMarseilleFrance
  4. 4.APHM, CHU TimoneCentre de Référence des Maladies Neuromusculaires et de la SLAMarseilleFrance

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