A Knowledge-Based Modality-Independent Technique for Concurrent Thigh Muscle Segmentation: Applicable to CT and MR Images


The mass of the lower extremity muscles is a clinically significant metric. Manual segmentation of these muscles is a time-consuming task. Most of the segmentation methods for the thigh muscles are based on statistical models and atlases which need manually segmented datasets. The goal of this work is an automatic segmentation of the thigh muscles with only one initial segmented slice. A new automatic method is proposed for concurrent individual thigh muscles segmentation using a hybrid level set method and anatomical information of the muscles. In the proposed method, the muscle regions are extracted by the Fast and Robust Fuzzy C-Means Clustering (FRFCM) method, and then a contour is determined for each muscle which changes according to the muscle shape variation through its length. The anatomical information is used to control the contours variations and to refine the final boundaries. The method was validated by 22 CT datasets. The average dice similarity coefficient (DSC) of the method for individual muscle segmentation with one and two initial slices were 89.29 ± 2.59 (%) and 91.77 ± 1.87 (%), respectively. Also, the average symmetric surface distances (ASSDs) were 0.93 ± 0.29 mm and 0.64 ± 0.18 mm. Furthermore, applying to ten MRI datasets, the average DSC and ASSD for muscles were 90.9 ± 2.61 (%) and 0.71 ± 0.33 mm, respectively. The quantitative and intuitive results of the proposed method show the effectiveness of this method in segmentation of large and small muscles in CT and MR images. The consumed computation time is lower than the previous works, and this method does not need any training datasets.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. 1.

    McDermott MM, Ferrucci L, Guralnik J, Tian L, Liu K, Hoff F, Liao Y, Criqui MH: Pathophysiological changes in calf muscle predict mobility loss at 2-year follow-up in men and women with peripheral arterial disease. Circulation 120(12):1048-1055,2009

    Article  Google Scholar 

  2. 2.

    Seymour J, Spruit M, Hopkinson N, Natanek S, Man W-C, Jackson A, Gosker H, Schols A, Moxham J, Polkey M: The prevalence of quadriceps weakness in COPD and the relationship with disease severity. Eur Respir J 36(1):81-88,2010

    CAS  Article  Google Scholar 

  3. 3.

    Emery AE: The muscular dystrophies. Lancet 359(9307) 687-695,2002

    CAS  Article  Google Scholar 

  4. 4.

    Yokota F, Otake Y, Takao M, Ogawa T, Okada T, Sugano N, Sato Y: Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method. Int J Comput Assist Radiol Surg 1-10,2018.

  5. 5.

    Uemura K, Takao M, Sakai T, Nishii T, Sugano N: Volume increases of the gluteus maximus, gluteus medius, and thigh muscles after hip arthroplasty. J Arthroplast 31(4):906-912.e1,2016

    Article  Google Scholar 

  6. 6.

    Andrews S, Hamarneh G: The generalized log-ratio transformation: learning shape and adjacency priors for simultaneous thigh muscle segmentation. IEEE Trans Med Imaging 34(9): 1773-1787,2015

    Article  Google Scholar 

  7. 7.

    Andrews S, Hamarneh G, Yazdanpanah A, HajGhanbari B, Reid WD: Probabilistic multi-shape segmentation of knee extensor and flexor muscles, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2011, pp. 651-658.

  8. 8.

    Jolivet E, Dion E, Rouch P, Dubois G, Charrier R, Payan C, Skalli W: Skeletal muscle segmentation from MRI dataset using a model-based approach. Comput Methods Biomech Biomed Eng 2(3) (2014) 138-145.

    Google Scholar 

  9. 9.

    Baudin P-Y, Azzabou N, Carlier PG, Paragios N: Automatic skeletal muscle segmentation through random walks and graph-based seed placement, Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on, IEEE, 2012, pp. 1036-1039.

  10. 10.

    Baudin P-Y, Azzabou N, Carlier PG, Paragios N: Prior knowledge, random walks and human skeletal muscle segmentation, International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, 2012, pp. 569-576.

  11. 11.

    Südhoff I, de Guise JA, Nordez A, Jolivet E, Bonneau D, Khoury V, Skalli W: 3D-patient-specific geometry of the muscles involved in knee motion from selected MRI images. Med Biol Eng Comput 47(6):579-587,2009.

    Article  Google Scholar 

  12. 12.

    Le Troter A, Fouré A, Guye M, Confort-Gouny S, Mattei J-P, Gondin J, Salort-Campana E, Bendahan D: Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches. MAGMA 29(2) (2016) 245-257.

    Article  Google Scholar 

  13. 13.

    Yokota F: Automated muscle segmentation from 3D CT data of the hip using a hierarchical multi-atlas method, 12th annual meeting of CAOS-international proceedings, 2012, pp. 30-32.

  14. 14.

    Orgiu S, Lafortuna CL, Rastelli F, Cadioli M, Falini A, Rizzo G: Automatic muscle and fat segmentation in the thigh from T1-Weighted MRI. J Magn Reson Imaging 43(3):601-610,2016.

    Article  Google Scholar 

  15. 15.

    Tan C, Yan Z, Zhang S, Belaroussi B, Yu HJ, Miller C, Metaxas DN: An automated and robust framework for quantification of muscle and fat in the thigh, Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE, 2014, pp. 3173-3178.

  16. 16.

    Karlsson A, Rosander J, Romu T, Tallberg J, Grönqvist A, Borga M, Dahlqvist Leinhard O: Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water–fat MRI. J Magn Reson Imaging 41(6):1558-1569,2015.

    Article  Google Scholar 

  17. 17.

    Yao J, Kovacs W, Hsieh N, Liu C-Y, Summers RM: Holistic segmentation of intermuscular adipose tissues on thigh MRI, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2017, pp. 737-745.

  18. 18.

    Ghosh S, Ray N, Boulanger P: A Structured Deep-Learning Based Approach for the Automated Segmentation of Human Leg Muscle from 3D MRI, 2017 14th Conference on Computer and Robot Vision (CRV), IEEE, 2017, pp. 117-123.

  19. 19.

    Ahmad E, Goyal M, McPhee JS, Degens H, Yap MH: Semantic segmentation of human thigh quadriceps muscle in magnetic resonance images, arXiv preprint arXiv:1801.00415 (2018).

  20. 20.

    Blaak E: Gender differences in fat metabolism, Curr Opin Clin Nutr Metab Care 4(6):499-502,2001.

    CAS  Article  Google Scholar 

  21. 21.

    Otake Y, Yokota F, Fukuda N, Takao M, Takagi S, Yamamura N, O’Donnell LJ, Westin C-F, Sugano N, Sato Y: Patient-Specific Skeletal Muscle Fiber Modeling from Structure Tensor Field of Clinical CT Images, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2017, pp. 656-663.

  22. 22.

    Kemnitz J, Eckstein F, Culvenor AG, Ruhdorfer A, Dannhauer T, Ring-Dimitriou S, Sänger AM, Wirth W: Validation of an active shape model-based semi-automated segmentation algorithm for the analysis of thigh muscle and adipose tissue cross-sectional areas. MAGMA 30(5):489-503,2017.

    CAS  Article  Google Scholar 

  23. 23.

    Prescott JW, Best TM, Swanson MS, Haq F, Jackson RD, Gurcan MN: Anatomically anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative. J Digit Imaging 24(1):28-43,2011

    Article  Google Scholar 

  24. 24.

    Kemnitz J, Eckstein F, Culvenor A, Ruhdorfer A, Dannhauer T, Ring-Dimitriou S, Sänger A, Wirth W: Validation of a 3D thigh muscle and adipose tissue segmentation method using statistical shape models. Osteoarthr Cartil 26:S457-S458,2018.

    Article  Google Scholar 

  25. 25.

    Kistler M, Bonaretti S, Pfahrer M, Niklaus R, Büchler P:The virtual skeleton database: an open access repository for biomedical research and collaboration. J Med Internet Res 15(11):2013

  26. 26.

    Kroon D-J, Slump CH, Maal TJ: Optimized anisotropic rotational invariant diffusion scheme on cone-beam CT, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2010, pp. 221-228.

  27. 27.

    Abdolali F, Zoroofi RA, Otake Y, Sato Y: Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 72 (2016) 108-119.

    Article  Google Scholar 

  28. 28.

    Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC: N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310-1320,2010

    Article  Google Scholar 

  29. 29.

    J.C. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means clustering algorithm, Comput Geosci 10(2-3) (1984) 191-203.

    Article  Google Scholar 

  30. 30.

    Lei T, Jia X, Zhang Y, He L, Meng H, Nandi AK: Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans Fuzzy Syst, 2018

  31. 31.

    Kroon D-J, Slump CH: MRI modalitiy transformation in demon registration, Biomedical Imaging: From Nano to Macro, 2009. ISBI'09. IEEE International Symposium on, IEEE, 2009, pp. 963-966.

  32. 32.

    Chan TF, Vese LA: Active contours without edges. IEEE Trans Image Process 10(2):266-277,2001

    CAS  Article  Google Scholar 

  33. 33.

    Taha AA, Hanbury A: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool, BMC Med Imaging 15(1):29,2015

    Article  Google Scholar 

  34. 34.

    Bischof H, Raicu D, Rau A: Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets, 2009.

Download references


The authors would like to sincerely acknowledge the valuable comments and supports of Professor Yoshinobu Sato and Dr. Yoshito Otake, both from Nara Institute of Science and Technology (NAIST), Japan, throughout this research.

Author information



Corresponding author

Correspondence to Malihe Molaie.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Molaie, M., Zoroofi, R.A. A Knowledge-Based Modality-Independent Technique for Concurrent Thigh Muscle Segmentation: Applicable to CT and MR Images. J Digit Imaging (2020). https://doi.org/10.1007/s10278-020-00354-w

Download citation


  • Thigh muscle segmentation
  • Knowledge-based level set
  • MRI
  • Multi-slice CT images