Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

  • Futoshi Yokota
  • Yoshito OtakeEmail author
  • Masaki Takao
  • Takeshi Ogawa
  • Toshiyuki Okada
  • Nobuhiko Sugano
  • Yoshinobu Sato
Original Article



Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.


We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.


The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).


We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.


Multi-atlas label fusion Musculoskeletal segmentation Hierarchical strategy 



This research was supported by MEXT/JSPS KAKENHI 26108004, 25242051 and 16K01411, JST PRESTO 20407, and AMED/ETH the strategic Japanese-Swiss cooperative research program.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Human and animals rights

This study has been approved by the Institutional Review Board of Osaka University Hospital (No. 15056), where the patients’ medical data used in this work were obtained.


  1. 1.
    Andrews S, Hamarneh G (2015) The generalized log-ratio transformation: learning shape and adjacency priors for simultaneous thigh muscle segmentation. IEEE Trans Med Imaging 34(9):1773–1787CrossRefPubMedGoogle Scholar
  2. 2.
    Arthofer C, Morgan PS, Pitiot A (2016) Hierarchical multi-atlas segmentation using label-specific embeddings, target-specific templates and patch refinement. In: Patch-based techniques in medical imaging LNCS 9993, pp 84–91Google Scholar
  3. 3.
    Baudin PY, Azzabou N, Carlier PG, Paragios N (2012) Prior knowledge, random walks and human skeletal muscle segmentation. In: International conference on medical image computing and computer-assisted intervention: MICCAI 2012, vol 15(Pt 1), pp 569–576Google Scholar
  4. 4.
    Diaz-Boladeras M, Angulo C, Domènech M, Albo-Canals J, Serrallonga N, Raya C, Barco A (2016) XIV Mediterranean conference on medical and biological engineering and computing 2016: MEDICON 2016. In: Conference on Medical and Biological Engineering and Computing, pp. 1179–1184Google Scholar
  5. 5.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRefGoogle Scholar
  6. 6.
    Fukuda N, Otake Y, Takao M, Yokota F, Ogawa T, Nakaya R, Tamura K, Grupp R, Farvardin A, Sugano N, Sato Y (2016) Statistical estimation of attachment of hip muscles based on measurement in cadavers. In: 16th annual meeting of CAOS-international proceedings, pp. 351–354Google Scholar
  7. 7.
    Gilles B, Magnenat-Thalmann N (2010) Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med Image Anal 14(3):291–302CrossRefPubMedGoogle Scholar
  8. 8.
    Glocker B, Komodakis N Drop. URL Accessed 5 Apr 2018
  9. 9.
    Glocker B, Sotiras A, Komodakis N, Paragios N (2011) Deformable medical image registration: setting the state of the art with discrete methods. Ann Rev Biomed Eng 13:219–244CrossRefGoogle Scholar
  10. 10.
    Guess TM, Stylianou AP, Kia M (2014) Concurrent prediction of muscle and tibiofemoral contact forces during treadmill gait. J Biomech Eng 136(2):021–032CrossRefGoogle Scholar
  11. 11.
    Huo Y, Plassard AJ, Carass A, Resnick SM, Pham DL, Prince JL, Landman BA (2016) Consistent cortical reconstruction and multi-atlas brain segmentation. NeuroImage 138:197–210CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Isgum I, Staring M, Rutten A, Prokop M, Viergever Ma, van Ginneken B (2009) Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging 28(7):1000–10CrossRefPubMedGoogle Scholar
  13. 13.
    Kamiya N, Muramatsu C, Zhou X, Chen H, Yokoyama R, Hara T (2013) Model-based approach to recognize the rectus abdominis muscle in CT by use of a virtual image-unfolding technique. IEICE Trans Inf Syst E–96–D(4):2–3Google Scholar
  14. 14.
    Karlsson A, Rosander J, Romu T, Tallberg J, Grönqvist A, Borga M, Dahlqvist Leinhard O (2014) Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging 41(6):1558–1569CrossRefPubMedGoogle Scholar
  15. 15.
    Kohout J, Clapworthy GJ, Zhao Y, Tao Y, Gonzalez-Garcia G, Dong F, Wei H, Kohoutová E (2013) Patient-specific fibre-based models of muscle wrapping. Interface Focus 3(2):20120,062CrossRefGoogle Scholar
  16. 16.
    Le Troter A, Fouré A, Guye M, Confort-Gouny S, Mattei JP, Gondin J, Salort-Campana E, Bendahan D (2016) Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches. Magn Reson Mater Phys Biol Med 29(2):245–257CrossRefGoogle Scholar
  17. 17.
    Ledig C, Heckemann RA, Hammers A, Lopez JC, Newcombe VF, Makropoulos A, Lötjönen J, Menon DK, Rueckert D (2015) Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 21(1):40–58CrossRefPubMedGoogle Scholar
  18. 18.
    Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, Do S (2017) Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging 30(4):487–498CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape location and unsupervised intensity priors. Med Image Anal 26(1):1–18CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Ou Y, Doshi J (2012) Multi-atlas segmentation of the prostate: a zooming process with robust registration and atlas selection. MICCAI Grand Chall Prostate MR Image Segmen 7:1–7Google Scholar
  21. 21.
    Popuri K, Cobzas D, Esfandiari N, Baracos V, Jägersand M (2016) Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging 35(2):512–520CrossRefPubMedGoogle Scholar
  22. 22.
    Rasch A, Bystrom AH, Dalen N, Martinez-Carranza N, Berg HE (2009) Persisting muscle atrophy two years after replacement of the hip. J Bone Joint Surg Br 91–B(5):583–588CrossRefGoogle Scholar
  23. 23.
    Rueckert D, Schnabel J IRTK. Accessed 5 Apr 2018
  24. 24.
    Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–21CrossRefPubMedGoogle Scholar
  25. 25.
    Styner M, Lee J, Chin B, Chin M (2008) 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation. Midas J 1–6Google Scholar
  26. 26.
    Takao M, Ogawa T, Yokota F, Otake Y, Hamada H, Sakai T, Sato Y, Sugano N (2017) Pre-operative fatty degeneration of gluteus minimus predicts falls after tha. Bone Joint J 99(SUPP 6):39–39Google Scholar
  27. 27.
    Thelen DG, Won Choi K, Schmitz AM (2014) Co-simulation of neuromuscular dynamics and knee mechanics during human walking. J Biomech Eng 136(2):021,033CrossRefGoogle Scholar
  28. 28.
    Uemura K, Takao M, Sakai T, Nishii T, Sugano N (2016) Volume increases of the gluteus maximus, gluteus medius, and thigh muscles after hip arthroplasty. J Arthroplasty 31(4):906–912.e1CrossRefPubMedGoogle Scholar
  29. 29.
    Webb JD, Blemker SS, Delp SL (2014) 3D finite element models of shoulder muscles for computing lines of actions and moment arms. Comput Methods Biomech Biomed Eng 17(8):829–37CrossRefGoogle Scholar
  30. 30.
    Wu D, Ma T, Ceritoglu C, Li Y, Chotiyanonta J, Hou Z, Hsu J, Xu X, Brown T, Miller MI, Mori S (2016) Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. NeuroImage 125:120–130CrossRefPubMedGoogle Scholar
  31. 31.
    Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2013) Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. In: International conference on medical image computing and computer-assisted intervention: MICCAI 2013, pp 190–197Google Scholar
  32. 32.
    Yokota F, Takaya M, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2012) Automated muscle segmentation from 3D CT data of the hip using hierarchical multi-atlas method. In: The 12th annual meeting of the international society for computer assisted orthopaedic surgery: CAOS 2012, vol 1, pp 16–18Google Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Graduate School of Information ScienceNara Institute of Science and TechnologyIkomaJapan
  2. 2.Graduate School of MedicineOsaka UniversitySuitaJapan
  3. 3.Faculty of MedicineUniversity of TsukubaTsukubaJapan

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