Skip to main content
SpringerLink
Log in

Automated quantification of glenoid bone loss in CT scans for shoulder dislocation surgery planning

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

Abstract

Purpose

Estimation of glenoid bone loss in CT scans following shoulder dislocation is required to determine the type of surgery needed to restore shoulder stability. This paper presents a novel automatic method for the computation of glenoid bone loss in CT scans.

Methods

The model-based method is a pipeline that consists of four steps: (1) computation of an oblique plane in the CT scan that best matches the glenoid face orientation; (2) selection of the glenoid oblique CT slice; (3) computation of the circle that best fits the posteroinferior glenoid contour; (4) quantification of the glenoid bone loss. The best-fit circle is computed with newly defined Glenoid Clock Circle Constraints.

Results

The pipeline and each of its steps were evaluated on 51 shoulder CT scans (44 patients). Ground truth oblique slice, best-fit circle, and glenoid bone loss measurements were obtained manually from three clinicians. The full pipeline yielded a mean absolute error (%) for the bone loss deficiency of 2.3 ± 2.9 mm (4.67 ± 3.32%). The mean oblique CT slice selection difference was 1.42 ± 1.32 slices, above the observer variability of 1.74 ± 1.82 slices. The glenoid bone loss deficiency measure (%) on the ground truth oblique glenoid CT slice has a mean average error of 0.54 ± 1.03 mm (4.76 ± 3.00%), close to the observer variability of 0.93 ± 1.40 mm (2.98 ± 4.97%).

Conclusion

Our pipeline is the first fully automatic method for the quantitative analysis of glenoid bone loss in CT scans. The computed glenoid bone loss report may assist orthopedists in selecting and planning surgical shoulder dislocation procedures.

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

Access this article

Log in via an institution

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. Zacchilli MA, Owens BD (2010) Epidemiology of shoulder dislocations presenting to emergency departments in the United States. J Bone Joint Surg 92(3):542–549

    Article  PubMed  Google Scholar 

  2. Cutts S, Prempeh M, Drew S (2009) Anterior shoulder dislocation. Ann R Coll Surg Engl 91(1):2–7

    Article  PubMed  PubMed Central  Google Scholar 

  3. Rowe CR, Patel D, Southmayd WW (1978) The Bankart procedure: a long-term end-result study. J Bone Joint Surg 60(1):1–16

    Article  CAS  PubMed  Google Scholar 

  4. Latarjet M (1954) A propos du traitement des luxations recidivantes de l’epaule. Lyon Chir 49:994–997

    CAS  PubMed  Google Scholar 

  5. Chuang TY, Adams CR, Burkhart SS (2008) Use of preoperative three-dimensional computed tomography to quantify glenoid bone loss in shoulder instability. Arthrosc J Arthrosc Relat Surg 24(4):376–382

    Article  Google Scholar 

  6. Dumont GD, Russell RD, Browne MG, Robertson WJ (2012) Area-based determination of bone loss using the glenoid arc angle. Arthrosc J Arthrosc Relat Surg 28(7):1030–1035

    Article  Google Scholar 

  7. Green GL, Arnander M, Pearse E, Tennent D (2022) CT estimation of glenoid bone loss in anterior glenohumeral instability: a systematic review of existing techniques. Bone Joint Open 3(2):114–122

    Article  PubMed  PubMed Central  Google Scholar 

  8. Griffith JF, Antonio GE, Tong CW, Ming CK (2003) Anterior shoulder dislocation: quantification of glenoid bone loss with CT. Am J Roentgenol 180(5):1423–1430

    Article  Google Scholar 

  9. Sugaya H (2010) Instability with bone loss. In: Angelo RL, Esch JC, Ryu RK, eds. AANA advanced arthroscopy: the shoulder, vol 14, pp 136–146

  10. Baudi P, Righi P, Bolognesi D, Rivetta S, Urtoler ER, Guicciardi N, Carrara M (2005) How to identify and calculate glenoid bone deficit. La Chirurgia Degli Organi di Movimento 90(2):145–152

    CAS  PubMed  Google Scholar 

  11. Zhang J, Yan CH, Chui CK, Ong SH (2010) Fast segmentation of bone in CT images using 3D adaptive thresholding. Comput Biol Med 40(2):231–236

    Article  CAS  PubMed  Google Scholar 

  12. Taghizadeh E, Terrier A, Becce F, Farron A, Büchler P (2019) Automated CT bone segmentation using statistical shape modelling and local template matching. Comput Methods Biomech Biomed Eng 22(16):1303–1310

    Article  Google Scholar 

  13. Mahdi FP, Tanaka H, Nobuhara K, Kobashi S (2020) Automatic segmentation of the humerus region in 3-D shoulder CT images using U-Net. Int J Biomed Soft Comput Hum Sci 25(2):67–74

    Google Scholar 

  14. Noguchi S, Nishio M, Yakami M, Nakagomi K, Togashi K (2020) Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques. Comput Biol Med 121:103767

    Article  PubMed  Google Scholar 

  15. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

  16. Yushkevich PA, Gao Y, Gerig G (2016) ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: proceedings of 38th annual international conference of the IEEE engineering in medicine and biology society, pp, 3342–3345

  17. Linden FV, Schmid K, Rommes E (2007) Philips medical systems. Software product lines in action. Springer, Berlin, pp 233–248

    Google Scholar 

  18. Rabinowitz J, Friedman R, Eichinger JK (2017) Management of glenoid bone loss with anterior shoulder instability: indications and outcomes. Curr Rev Musculoskelet Med 10(4):452–462

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bakshi NK, Cibulas GA, Sekiya JK, Bedi A (2018) A clinical comparison of linear and surface area–based methods of measuring glenoid bone loss. Am J Sports Med 46(10):2472–2477

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Ori Safran and Leo Joskowicz have equal contribution.

Authors and Affiliations

  1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel

    Avichai Haimi & Leo Joskowicz

  2. Department of Orthopedics, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel

    Shaul Beyth, Moshe Gross & Ori Safran

Authors
  1. Avichai Haimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaul Beyth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moshe Gross
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ori Safran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leo Joskowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leo Joskowicz.

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.

Informed consent

There was no need for informed consent required for the work reported in this manuscript.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haimi, A., Beyth, S., Gross, M. et al. Automated quantification of glenoid bone loss in CT scans for shoulder dislocation surgery planning. Int J CARS 19, 129–137 (2024). https://doi.org/10.1007/s11548-023-02995-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-023-02995-y

Keywords

Search

Navigation