Advertisement

Fully Automatic Planning of Total Shoulder Arthroplasty Without Segmentation: A Deep Learning Based Approach

  • Paul Kulyk
  • Lazaros Vlachopoulos
  • Philipp Fürnstahl
  • Guoyan ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11404)

Abstract

We present a method for automatically determining the position and orientation of the articular marginal plane (AMP) of the proximal humerus in computed tomography (CT) images without segmentation or hand-crafted features. The process is broken down into 3 stages. Stage 1 determines a coarse estimation of the AMP center by sampling patches over the entire image and combining predictions with a novel kernel density estimation method. Stage 2 utilizes the estimate from stage 1 to focus on a smaller sampling region and operates at a higher images resolution to obtain a refined prediction of the AMP center. Stage 3 focuses patch sampling on the region around the center obtained at stage 2 and regresses the tip of a vector normal to the AMP which yields the orientation of the plane. The system was trained and evaluated on 27 upper arm CTs. In a 4-fold cross-validation the mean error in estimating the AMP center was \(1.30\,{\pm }\,0.65\) mm and the angular error for estimating the normal vector was \(4.68\,{\pm }\,2.84^\circ \).

Keywords

Regression Proximal humerus Articular marginal plane Deep learning Total shoulder arthroplasty 

References

  1. 1.
    Kim, S., Wise, B., Zhang, Y., Szabo, R.: Increasing incidence of shoulder arthroplasty in the United States. J. Bone Joint Surg. Am. 93(24), 2249–2254 (2011).  https://doi.org/10.2106/JBJS.J.01994CrossRefGoogle Scholar
  2. 2.
    Keener, J., Chalmers, P., Yamaguchi, K.: The humeral implant in shoulder arthroplasty. J. Am. Acad. Orthop. Surg. 25(6), 427–438 (2017).  https://doi.org/10.5435/JAAOS-D-15-00682CrossRefGoogle Scholar
  3. 3.
    Edwards, T., Morris, B., Gartsman, G.: Shoulder Arthroplasty, 2nd edn. Elsevier, Amsterdam (2019)Google Scholar
  4. 4.
    Dines, D., Laurencin, C., Williams, G. (eds.): Arthritis & Arthroplasty: The Shoulder. Saunders/Elsevier, Philadelphia (2009)Google Scholar
  5. 5.
    Pearl, M.: Proximal humeral anatomy in shoulder arthroplasty: implications for prosthetic design and surgical technique. J. Shoulder Elbow Surg. 14(Suppl 1), S99–S104 (2005).  https://doi.org/10.1016/j.jse.2004.09.025CrossRefGoogle Scholar
  6. 6.
    DeLude, J., et al.: An anthropometric study of the bilateral anatomy of the humerus. J. Shoulder Elbow Surg. 16(4), 477–483 (2007).  https://doi.org/10.1016/j.jse.2006.09.016CrossRefGoogle Scholar
  7. 7.
    Johnson, J., Thostenson, J., Suva, L., Hasan, S.: Relationship of bicipital groove rotation with humeral head retroversion: a three-dimensional computed tomographic analysis. J. Bone Joint Surg. Am. 95(8), 719–724 (2013).  https://doi.org/10.2106/JBJS.J.00085CrossRefGoogle Scholar
  8. 8.
    Vlachopoulos, L., et al.: Computer algorithms for three-dimensional measurement of humeral anatomy: analysis of 140 paired humeri. J. Shoulder Elbow Surg. 25(2), e38–e48 (2016).  https://doi.org/10.1016/j.jse.2015.07.027CrossRefGoogle Scholar
  9. 9.
    Tschannen, M., Vlachopoulos, L., Gerber, C., Székely, G., Fürnstahl, P.: Regression forest-based automatic estimation of the articular margin plane for shoulder prosthesis planning. Med. Image Anal. 31, 88–97 (2016).  https://doi.org/10.1016/j.media.2016.02.008CrossRefGoogle Scholar
  10. 10.
    Janssens, R., Zeng, G., Zheng, G.: Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks. arXiv:1712.01509 (2017). http://arxiv.org/abs/1712.01509
  11. 11.
    Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_27CrossRefGoogle Scholar
  12. 12.
    Zhang, J., et al.: Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 720–728. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_81CrossRefGoogle Scholar
  13. 13.
    Boileau, P., Cheval, D., Gauci, M., Holzer, N., Chaoui, J., Walch, G.: Automated three-dimensional measurement of glenoid version and inclination in arthritic shoulders. J. Bone Joint Surg. Am. 100(1), 57–65 (2018).  https://doi.org/10.2106/JBJS.16.01122CrossRefGoogle Scholar
  14. 14.
    Nguyen, D., et al.: Improved accuracy of computer assisted glenoid implantation in total shoulder arthroplasty: an in-vitro randomized controlled trial. J. Shoulder Elbow Surg. 18(6), 907–914 (2009).  https://doi.org/10.1016/j.jse.2009.02.022CrossRefGoogle Scholar
  15. 15.
    Werner, B., Hudek, R., Burkhart, K., Gohlke, F.: The influence of three-dimensional planning on decision-making in total shoulder arthroplasty. J. Shoulder Elbow Surg. 26(8), 1477–1483 (2017).  https://doi.org/10.1016/j.jse.2017.01.006CrossRefGoogle Scholar
  16. 16.
    Suzani, A., Seitel, A., Liu, Y., Fels, S., Rohling, R.N., Abolmaesumi, P.: Fast automatic vertebrae detection and localization in pathological CT scans - a deep learning approach. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 678–686. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_81CrossRefGoogle Scholar
  17. 17.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014). http://arxiv.org/abs/1412.6980
  18. 18.
    Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. arXiv:1605.08695 (2016). http://arxiv.org/abs/1605.08695

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul Kulyk
    • 1
    • 2
  • Lazaros Vlachopoulos
    • 3
  • Philipp Fürnstahl
    • 3
  • Guoyan Zheng
    • 1
    Email author
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.College of MedicineUniversity of SaskatchewanSaskatoonCanada
  3. 3.Computer Assisted Research and Development GroupUniversity of Zurich, Balgrist University HospitalZurichSwitzerland

Personalised recommendations