Advertisement

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

Abstract

Objectives

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.

Results

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.

Conclusion

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.

Keywords

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

Notes

Acknowledgments

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)

References

  1. 1.
    Barnouin Y, Butler-Browne G, Voit T, Reversat D, Azzabou N, Leroux G, Behin A, McPhee JS, Carlier PG, Hogrel J-Y (2014) Manual segmentation of individual muscles of the quadriceps femoris using MRI: A reappraisal. J Magn Reson Imaging 40:239–247CrossRefPubMedGoogle Scholar
  2. 2.
    Baudin PY, Azzabou N, Carlier PG, Paragios N (2012) Prior knowledge, random walks and human skeletal muscle segmentation. Med Image Comput Comput Assist Interv 15:569–576PubMedGoogle Scholar
  3. 3.
    Baudin P-Y, Goodman D, Kumrnar P, Azzabou N, Carlier PG, Paragios N, Kumar MP (2013) Discriminative parameter estimation for random walks segmentation. Med Image Comput Comput Assist Interv 16:219–226PubMedGoogle Scholar
  4. 4.
    Makrogiannis S, Serai S, Fishbein KW, Schreiber C, Ferrucci L, Spencer RG (2012) Automated quantification of muscle and fat in the thigh from water-, fat-, and nonsuppressed MR images. J Magn Reson Imaging 35:1152–1161CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Prescott JW, Best TM, Swanson MS, Haq F, Jackson RD, Gurcan MN (2011) Anatomically anchored template-based level set segmentation: Application to quadriceps muscles in MR images from the osteoarthritis initiative. J Digit Imaging 24:28–43CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Ahmad E, Yap MH, Degens H, McPhee JS (2014) Atlas-registration based image segmentation of MRI human thigh muscles in 3D space. In: Proceedings of SPIE 9037. Medical imaging 2014: image perception, observer performance, and technology assessment. doi: 10.1117/12.2043606
  7. 7.
    Ma D, Cardoso MJ, Modat M, Powell N, Wells J, Holmes H, Wiseman F, Tybulewicz V, Fisher E, Lythgoe MF, Ourselin S (2014) Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion. PLoS One 9:e86576CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Eugenio Iglesias J, Rory Sabuncu M, Van Leemput K (2013) A unified framework for cross-modality multi-atlas segmentation of brain MRI. Med Image Anal 17:1181–1191CrossRefPubMedGoogle Scholar
  9. 9.
    Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33:115–126CrossRefPubMedGoogle Scholar
  10. 10.
    Wu G, Wang Q, Zhang D, Nie F, Huang H, Shen D (2014) A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med Image Anal 18:881–890CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Klein S, van der Heide UA, Lips IM, van Vulpen M, Staring M, Pluim JPW (2008) Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys 35:1407CrossRefPubMedGoogle Scholar
  12. 12.
    Gubern-Mérida A, Kallenberg M, Martí R, Karssemeijer N (2012) Segmentation of the pectoral muscle in breast MRI using atlas-based approaches. Med Image Comput Comput Assist Interv 15:371–378PubMedGoogle Scholar
  13. 13.
    Thomas MS, Newman D, Leinhard OD, Kasmai B, Greenwood R, Malcolm PN, Karlsson A, Rosander J, Borga M, Toms AP (2014) Test–retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system. Eur Radiol 24:2279–2291CrossRefPubMedGoogle Scholar
  14. 14.
    Karlsson A, Rosander J, Romu T, Tallberg J, Grönqvist A, Borga M, Dahlqvist Leinhard O (2015) Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging 41:1558–1569CrossRefPubMedGoogle Scholar
  15. 15.
    Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC (1994) Morphometric analysis of white matter lesions in MR images: Method and validation. IEEE Trans Med Imaging 13:716–724CrossRefPubMedGoogle Scholar
  16. 16.
    Udupa JK, LeBlanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, Hirsch BE, Woodburn J (2006) A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph 30:75–87CrossRefPubMedGoogle Scholar
  17. 17.
    Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62:782–790CrossRefPubMedGoogle Scholar
  18. 18.
    Positano V, Christiansen T, Santarelli MF, Ringgaard S, Landini L, Gastaldelli A (2009) Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh. J Magn Reson Imaging 29:677–684CrossRefPubMedGoogle Scholar
  19. 19.
    Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7:359–369CrossRefPubMedGoogle Scholar
  20. 20.
    Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans Med Imaging 18:712–721CrossRefPubMedGoogle Scholar
  21. 21.
    Modat M, Ridgway GR, Taylor ZA, Lehmann M, Barnes J, Hawkes DJ, Fox NC, Ourselin S (2010) Fast free-form deformation using graphics processing units. Comput Methods Programs Biomed 98:278–284CrossRefPubMedGoogle Scholar
  22. 22.
    Avants B, Epstein C, Grossman M, Gee J (2008) Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12:26–41CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Jorge Cardoso M, Leung K, Modat M, Keihaninejad S, Cash D, Barnes J, Fox NC, Ourselin S (2013) STEPS: similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation. Med Image Anal 17:671–684CrossRefPubMedGoogle Scholar
  24. 24.
    Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–921CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, Wells WM, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index 1: Scientific reports. Acad Radiol 11:178–189CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Nordez A, Jolivet E, Südhoff I, Bonneau D, de Guise JA, Skalli W (2009) Comparison of methods to assess quadriceps muscle volume using magnetic resonance imaging. J Magn Reson Imaging 30:1116–1123CrossRefPubMedGoogle Scholar
  27. 27.
    Hopkins WG (2000) Measures of reliability in sports medicine and science. Sports Med Auckl N Z 30:1–15CrossRefGoogle Scholar
  28. 28.
    Shrout PE, Fleiss JL (1979) Intraclass correlations: Uses in assessing rater reliability. Psychol Bull 86:420–428CrossRefPubMedGoogle Scholar
  29. 29.
    Reeves ND, Narici MV, Maganaris CN (2004) Effect of resistance training on skeletal muscle-specific force in elderly humans. J Appl Physiol (1985) 96:885–892CrossRefGoogle Scholar
  30. 30.
    Gondin J, Guette M, Ballay Y, Martin A (2005) Electromyostimulation training effects on neural drive and muscle architecture. Med Sci Sports Exerc 37:1291–1299CrossRefPubMedGoogle Scholar
  31. 31.
    Fouré A, Duhamel G, Wegrzyk J, Boudinet H, Mattei J-P, Le Troter A, Bendahan D, Gondin J (2015) Heterogeneity of muscle damage induced by electrostimulation: A multimodal MRI study. Med Sci Sports Exerc 47:166–175CrossRefPubMedGoogle Scholar
  32. 32.
    Thom JM, Thompson MW, Ruell PA, Bryant GJ, Fonda JS, Harmer AR, Janse de Jonge XA, Hunter SK (2001) Effect of 10-day cast immobilization on sarcoplasmic reticulum calcium regulation in humans. Acta Physiol Scand 172:141–147CrossRefPubMedGoogle Scholar
  33. 33.
    Vandenborne K, Elliott MA, Walter GA, Abdus S, Okereke E, Shaffer M, Tahernia D, Esterhai JL (1998) Longitudinal study of skeletal muscle adaptations during immobilization and rehabilitation. Muscle Nerve 21:1006–1012CrossRefPubMedGoogle Scholar
  34. 34.
    Willis TA, Hollingsworth KG, Coombs A, Sveen M-L, Andersen S, Stojkovic T, Eagle M, Mayhew A, de Sousa PL, Dewar L, Morrow JM, Sinclair CDJ, Thornton JS, Bushby K, Lochmuller H, Hanna MG, Hogrel J-Y, Carlier PG, Vissing J, Straub V (2014) Quantitative magnetic resonance imaging in limb-girdle muscular dystrophy 2I: A multinational cross-sectional study. PLoS one 9:e90377CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hollingsworth KG, Garrood P, Eagle M, Bushby K, Straub V (2013) Magnetic resonance imaging in Duchenne muscular dystrophy: Longitudinal assessment of natural history over 18 months: short reports. Muscle Nerve 48:586–588CrossRefPubMedGoogle Scholar
  36. 36.
    Maden-Wilkinson TM, Degens H, Jones DA, McPhee JS (2013) Comparison of MRI and DXA to measure muscle size and age-related atrophy in thigh muscles. J Musculoskelet Neuronal Interact 13:320–328PubMedGoogle Scholar
  37. 37.
    Hogrel J-Y, Barnouin Y, Azzabou N, Butler-Browne G, Voit T, Moraux A, Leroux G, Behin A, McPhee JS, Carlier PG (2015) NMR imaging estimates of muscle volume and intramuscular fat infiltration in the thigh: Variations with muscle, gender, and age. Age. doi: 10.1007/s11357-015-9798-5 Google Scholar

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

Personalised recommendations