Advertisement

Automated Recognition of Erector Spinae Muscles and Their Skeletal Attachment Region via Deep Learning in Torso CT Images

  • Naoki KamiyaEmail author
  • Masanori Kume
  • Guoyan Zheng
  • Xiangrong Zhou
  • Hiroki Kato
  • Huayue Chen
  • Chisako Muramatsu
  • Takeshi Hara
  • Toshiharu Miyoshi
  • Masayuki Matsuo
  • Hiroshi Fujita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11404)

Abstract

Erector spinae muscle (ESM) is an important muscle in the torso region. Changes of sizes, shapes and densities in the cross section of the spinal column muscles have been found in chronic low back pain, degenerative lumbar sclerosis and chronic obstructive pulmonary disease. However, the image features of the ESM are measured manually by the physician. Therefore, automatic recognition in three dimensions (3D) not only for the limited two-dimensional (2D) section but also for the whole ESM is required. In this study, we realize automatic recognition of the ESMs and its attachment region on the skeleton using a 2D deep convolutional neural network. Each cross section of the 3D computed tomography (CT) image is input as a 2D image to the fully convolutional network. Then, the obtained result is reconstructed into a 3D image to obtain the recognition result of the ESM and its attachment region on the skeleton. ESM and attached area are extracted manually from the CT images of 11 cases and used for evaluation. In the experiments, automatic recognition was performed for each case using the leave-one-out method. The mean recognition accuracy of ESM and attached area was \(89.9\%\) and \(65.5\%\), respectively for the Dice coefficient. In this study, although there is over-extraction in the recognition of the attachment region, the initial region has been acquired successfully and it is the first study to simultaneously recognize the ESMs and its attachment region on the skeleton.

Keywords

Erector spinae muscles Skeletal muscles Deep convolutional neural networks Fully convolutional networks 

Notes

Acknowledgements

This research was supported in part by a Grant-in-Aid for Scientific Research on Innovative Areas (Grant No. 26108005 and 17H05301), MEXT, Japan.

References

  1. 1.
    Danneels, L., Vanderstraeten, G., Cambier, D., Witvrouw, E., De Cuyper, H., Danneels, L.: CT imaging of trunk muscles in chronic low back pain patients and healthy control subjects. Eur. Spine J. 9(4), 266–272 (2000).  https://doi.org/10.1007/s005860000190CrossRefGoogle Scholar
  2. 2.
    Yagi, M., Hosogane, N., Watanabe, K., Asazuma, T., Matsumoto, M.: The paravertebral muscle and psoas for the maintenance of global spinal alignment in patient with degenerative lumbar scoliosis. Spine J. 16(4), 451–458 (2016).  https://doi.org/10.1016/j.spinee.2015.07.001CrossRefGoogle Scholar
  3. 3.
    Tanimura, K., et al.: Quantitative assessment of erector spinae muscles in patients with chronic obstructive pulmonary disease. Novel chest computed tomography-derived index for prognosis. Ann. Am. Thorac. Soc. 13(3), 334–341 (2016).  https://doi.org/10.1513/AnnalsATS.201507-446OCCrossRefGoogle Scholar
  4. 4.
    Wei, Y., Xu, B., Tao, X., Qu, J.: Paraspinal muscle segmentation in CT images using a single atlas. In: Proceedings of IEEE International Conference on Progress in Informatics and Computing – PIC 2015, pp. 211–215. IEEE (2015).  https://doi.org/10.1109/PIC.2015.7489839
  5. 5.
    Popuri, K., Cobzas, D., Esfandiari, N., Baracos, V., Jägersand, M.: Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans. Med. Imaging 35(2), 512–520 (2016).  https://doi.org/10.1109/TMI.2015.2479252CrossRefGoogle Scholar
  6. 6.
    Kume, M., et al.: Automated recognition of the erector spinae muscle based on deep CNN at the level of the twelfth thoracic vertebrae in torso CT images. In: Proceedings of 36th JAMIT Annual Meeting, pp. 74–76 (2017)Google Scholar
  7. 7.
    Kamiya, N., et al.: Automated segmentation of recuts abdominis muscle using shape model in X-ray CT images. In: Proceedings of 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society – EMBC 2011, pp. 7993–7996. IEEE (2011).  https://doi.org/10.1109/IEMBS.2011.6091971
  8. 8.
    Kamiya, N., et al.: Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol. Phys. Technol. 5(1), 5–14 (2012).  https://doi.org/10.1007/s12194-011-0127-0CrossRefGoogle Scholar
  9. 9.
    Kamiya, N., Li, J., Kume, M., Fujita, H., Shen, D., Zheng, G.: Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications. In: Proceedings of 32nd International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS 2018, pp. 18–00047 (2018)CrossRefGoogle Scholar
  10. 10.
    Yokota, F., et al.: Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method. Int. J. Comput. Assist. Radiol. Surg. 13(7), 977–986 (2018).  https://doi.org/10.1007/s11548-018-1758-yCrossRefGoogle Scholar
  11. 11.
    Katafuchi, T., et al.: Improvement of supraspinatus muscle recognition methods based on the anatomical features on the scapula in torso CT image. In: Proceedings of International Forum on Medical Imaging in Asia - IFMIA, pp. 315–316 (2017)Google Scholar
  12. 12.
    Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., Fujita, H.: Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 111–120. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46976-8_12CrossRefGoogle Scholar
  13. 13.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition – CVPR 2015, pp. 3431–3440. IEEE (2015).  https://doi.org/10.1109/CVPR.2015.7298965
  14. 14.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2015)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Naoki Kamiya
    • 1
    Email author
  • Masanori Kume
    • 2
  • Guoyan Zheng
    • 3
  • Xiangrong Zhou
    • 4
  • Hiroki Kato
    • 5
  • Huayue Chen
    • 6
  • Chisako Muramatsu
    • 4
  • Takeshi Hara
    • 4
  • Toshiharu Miyoshi
    • 7
  • Masayuki Matsuo
    • 8
  • Hiroshi Fujita
    • 4
  1. 1.School of Information Science and TechnologyAichi Prefecture UniversityNagakuteJapan
  2. 2.Graduate School of National Science and TechnologyGifu UniversityGifuJapan
  3. 3.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  4. 4.Department of Electrical, Electronic and Computer EngineeringGifu UniversityGifuJapan
  5. 5.Department of Radiology ServiceGifu University HospitalGifuJapan
  6. 6.School of MedicineUniversity of Occupational and Environmental HealthKitakyushuJapan
  7. 7.Radiology ServiceGifu University HospitalGifuJapan
  8. 8.Graduate School of Medicine, Department of RadiologyGifu UniversityGifuJapan

Personalised recommendations