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Muscle Segmentation for Orthopedic Interventions

  • Naoki Kamiya
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)

Abstract

Skeletal muscle segmentation techniques can help orthopedic interventions in various scenes. In this chapter, we describe two methods of skeletal muscle segmentation on 3D CT images. The first method is based on a computational anatomical model, and the second method is a deep learning-based method. The computational anatomy-based methods are modeling the muscle shape with its running and use it for segmentation. In the deep learning-based methods, the muscle regions are directly acquired automatically. Both approaches can obtain muscle regions including shape, area, volume, and some other image texture features. And it is desirable that the method be selected by the required orthopedic intervention. Here, we show each design philosophy and features of a representative method. We discuss the various examples of site-specific segmentation of skeletal muscle in non-contrast images using torso CT and whole-body CT including in cervical, thoracoabdominal, surface and deep muscles. And we also mention the possibility of application to orthopedic intervention.

Keywords

CT Skeletal muscle segmentation Orthopedic interventions Computational anatomy Deep learning Fully Convolutional Network (FCN) 

Notes

Acknowledgments

This work was supported in part by a JSPS Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy, #26108005 and # 17H05301) and a JSPS Grant-in-Aid for Young Scientists (B) (#15K21588) and for Challenging Exploratory Research (#16K15346), JAPAN.

References

  1. 1.
    Kobatake H, Masutani Y et al (2017) Computational anatomy based on whole body imaging: basic principles of computer-assisted diagnosis and therapy, Springer. https://www.springer.com/us/book/9784431559740
  2. 2.
    Fujita H, Hara T, Zhou X et al (2014) Model construction for computational anatomy: progress overview FY2009-FY2013. In: Proceeding of the fifth international symposium on the project “Computational Anatomy”, pp 25–35. http://www.fjt.info.gifu-u.ac.jp/publication/803.pdf
  3. 3.
    Hanaoka S, Kamiya N, Sato Y et al (2017) Skeletal muscle, understanding medical images based on computational anatomy models, pp 165–171, Springer. https://link.springer.com/chapter/10.1007/978-4-431- 55976-4_3
  4. 4.
    Fujita H, Hara T, Zhou X et al (2015) Function integrated diagnostic assistance based on multidisciplinary computational anatomy – plan of five years and progress overview FY2014 -. In: Proceeding of the first international symposium on the project “Multidisciplinary Computational Anatomy”, pp 45–51Google Scholar
  5. 5.
    Fujita H, Hara T, Zhou X et al (2016) Function integrated diagnostic assistance based on multidisciplinary computational anatomy models -Progress overview FY 2015-. In: Proceeding of the second international symposium on the project “Multidisciplinary Computational Anatomy”, pp 91–101Google Scholar
  6. 6.
    Fujita H, Hara T, Zhou X et al (2017) Function integrated diagnostic assistance based on multidisciplinary computational anatomy models. In: Proceeding of the third international symposium on the project “Multidisciplinary Computational Anatomy”, pp 95–105Google Scholar
  7. 7.
    Kamiya N, Zhou X, Chen H et al (2012) Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol Phys Technol 5(1):5–14CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Kamiya N, Zhou X, Azuma K et al (2016) Automated recognition of the iliac muscle and modeling of muscle fiber direction in torso CT images. In: Proceeding of SPIE medical imaging 2016, Computer-Aided Diagnosis, 9785, 97853K-97853K-4,  https://doi.org/10.1117/12.2214613
  9. 9.
    Zhou X, Takayama R, Wang S et al (2017) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10): 5221–5233CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Kume M, Kamiya N, Zhou X et al (2017) Automatic recognition of erector spinae muscle in torso CT image and its possibility as prognostic predictor of COPD. In: Proceeding of the 9th annual meeting of Japanese Society of Pulmonary Functional Imaging, 47Google Scholar
  11. 11.
    Zhou X, Ito T, Takayama R et al (2016) Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting, proceeding of workshop on the 2nd Deep Learning in Medical Image Analysis (DLMIA) in MICCAI 2016. LNCS 10008: 111–120Google Scholar
  12. 12.
    Zhou X, Takayama R, Wang S et al (2017) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10): 5221–5233CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Kamiya N, Ieda K, Zhou X et al (2017) Automated analysis of whole skeletal muscle for muscular atrophy detection of ALS in whole-body CT images: preliminary study, Proc. of SPIE Medical Imaging 2017, Computer-Aided Diagnosis, 10134, 1013442-1-1013442-6.  https://doi.org/10.1117/12.2251584
  14. 14.
    Kamiya N, Asano E, Zhou X et al (2017) Segmental recognition of skeletal muscle in whole-body CT images and its texture analysis using skeletal muscle models. Int J Comput Assist Radiol Surg 12(1): S275Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Aichi Prefectural UniversityNagakuteJapan

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