Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Automatic bone surfaces segmentation is one of the fundamental tasks of ultrasound (US)-guided computer-assisted orthopedic surgery procedures. However, due to various US imaging artifacts, manual operation of the transducer during acquisition, and different machine settings, many existing methods cannot deal with the large variations of the bone surface responses, in the collected data, without manual parameter selection. Even for fully automatic methods, such as deep learning-based methods, the problem of dataset bias causes networks to perform poorly on the US data that are different from the training set.

Methods

In this work, an intensity-invariant convolutional neural network (CNN) architecture is proposed for robust segmentation of bone surfaces from US data obtained from two different US machines with varying acquisition settings. The proposed CNN takes US image as input and simultaneously generates two intermediate output images, denoted as local phase tensor (LPT) and global context tensor (GCT), from two branches which are invariant to intensity variations. LPT and GCT are fused to generate the final segmentation map. In the training process, the LPT network branch is supervised by precalculated ground truth without manual annotation.

Results

The proposed method is evaluated on 1227 in vivo US scans collected using two US machines, including a portable handheld ultrasound scanner, by scanning various bone surfaces from 28 volunteers. Validation of proposed method on both US machines not only shows statistically significant improvements in cross-machine segmentation of bone surfaces compared to state-of-the-art methods but also achieves a computation time of 30 milliseconds per image, \(98.5\%\) improvement over state-of-the-art.

Conclusion

The encouraging results obtained in this initial study suggest that the proposed method is promising enough for further evaluation. Future work will include extensive validation of the method on new US data collected from various machines using different acquisition settings. We will also evaluate the potential of using the segmented bone surfaces as an input to a point set-based registration method.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Hacihaliloglu I (2017) Ultrasound imaging and segmentation of bone surfaces: a review. Technology 5(02):74–80

    Article  Google Scholar 

  2. Yamauchi M, Kawaguchi R, Sugino S, Yamakage M, Honma E, Namiki A (2009) Ultrasound-aided unilateral epidural block for single lower-extremity pain. J Anesth 23(4):605–608

    Article  Google Scholar 

  3. Seitel A, Sojoudi S, Osborn J, Rasoulian A, Nouranian S, Lessoway VA, Rohling RN, Abolmaesumi P (2016) Ultrasound-guided spine anesthesia: feasibility study of a guidance system. Ultrasound Med Biol 42(12):3043–3049

    Article  Google Scholar 

  4. Anas EMA, Seitel A, Rasoulian A, John PS, Pichora D, Darras K, Wilson D, Lessoway VA, Hacihaliloglu I, Mousavi P, Rohling R, Abolmaesumi P (2015) Bone enhancement in ultrasound using local spectrum variations for guiding percutaneous scaphoid fracture fixation procedures. Int J Comput Assist Radiol Surg 10(6):959–969

    Article  Google Scholar 

  5. Wang P, Patel VM, Hacihaliloglu I (2018) Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided CNN. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 134–142

  6. Alsinan AZ, Patel VM, Hacihaliloglu I (2019) Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN. Int J Comput Assist Radiol Surg 14(5):775–783

    Article  Google Scholar 

  7. Villa M, Dardenne G, Nasan M, Letissier H, Hamitouche C, Stindel E (2018) FCN-based approach for the automatic segmentation of bone surfaces in ultrasound images. Int J Comput Assist Radiol Surg 13(11):1707–1716

    Article  CAS  Google Scholar 

  8. Schumann S (2016) State of the art of ultrasound-based registration in computer assisted orthopedic interventions. In: Zheng G, Li S (eds) Computational radiology for orthopaedic interventions. Springer, Cham, pp 271–297

    Chapter  Google Scholar 

  9. Hacihaliloglu I, Guy P, Hodgson AJ, Abugharbieh R (2015) Automatic extraction of bone surfaces from 3D ultrasound images in orthopaedic trauma cases. Int J Comput Assist Radiol Surg 10(8):1279–1287

    Article  Google Scholar 

  10. Baka N, Leenstra S, van Walsum T (2017) Ultrasound aided vertebral level localization for lumbar surgery. IEEE Trans Med Imaging 36(10):2138–2147

    Article  Google Scholar 

  11. Hacihaliloglu I, Rasoulian A, Rohling RN, Abolmaesumi P (2014) Local phase tensor features for 3-D ultrasound to statistical shape+ pose spine model registration. IEEE Trans Med Imaging 33(11):2167–2179

    Article  Google Scholar 

  12. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on machine learning (ICML-15), pp 448–456

  13. Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814

  14. Hacihaliloglu I (2017) Localization of bone surfaces from ultrasound data using local phase information and signal transmission maps. In: International workshop and challenge on computational methods and clinical applications in musculoskeletal imaging. Springer, pp 1–11

  15. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  16. Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the international conference on learning representations (ICLR)

  17. Pandey P, Patel H, Guy P, Hacihaliloglu I, Hodgson AJ (2019) Preliminary planning for a multi-institutional database for ultrasound bone segmentation. EPiC Ser Health Sci 3:297–300

    Article  Google Scholar 

Download references

Funding

This work was supported in part by 2017 North American Spine Society Young Investigator Award.

Author information

Authors and Affiliations

  1. Johns Hopkins University, Baltimore, USA

    Puyang Wang & Vishal M. Patel

  2. Rutgers New Jersey Medical School, Newark, USA

    Michael Vives

  3. Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, 08854, USA

    Ilker Hacihaliloglu

Authors
  1. Puyang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Vives
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vishal M. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ilker Hacihaliloglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ilker Hacihaliloglu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict 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.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, P., Vives, M., Patel, V.M. et al. Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN. Int J CARS 15, 1127–1135 (2020). https://doi.org/10.1007/s11548-020-02184-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-020-02184-1

Keywords

Search

Navigation