Statistical Shape Modeling Approach to Predict Missing Scapular Bone

  • Asma Salhi
  • Valerie Burdin
  • Arnaud Boutillon
  • Sylvain Brochard
  • Tinashe Mutsvangwa
  • Bhushan BorotikarEmail author
Original Article


Prediction of complete and premorbid scapular anatomy is an important aspect of successful shoulder arthroplasty surgeries to treat glenohumeral arthritis and which remains elusive in the current literature. We proposed to build a statistical shape model (SSM) of the scapula and use it to build a framework to predict a complete scapular shape from virtually created scapular bone defects. The bone defects were synthetically created to imitate bone loss in the glenoid region and missing bony part in inferior and superior scapular regions. Sixty seven dry scapulae were used to build the SSM while ten external scapular shapes (not used in SSM building) were selected to map scapular shape variability using its anatomical classification. For each external scapula, four virtual bone defects were created in the superior, inferior and glenoid regions by manually removing a part of the original mesh. Using these defective shapes as prior knowledge, original shapes were reconstructed using scapula SSM and Gaussian process regression. Robustness of the scapula SSM was excellent (generality = 0.79 mm, specificity = 1.74 mm, first 15 principal modes of variations accounted for 95% variability). The validity and quality of the reconstruction of complete scapular bone were evaluated using two methods (1) mesh distances in terms of mean and RMS values and (2) four anatomical measures (three angles: glenoid version, glenoid inclination, and critical shoulder angle, and glenoid center location). The prediction error in the angle measures ranged from 1.0° to 2.2°. For mesh distances, highest mean and RMS error was 0.97 mm and 1.30 respectively. DICE similarity coefficient between the original and predicted shapes was excellent (≥ 0.81). This framework provided high reconstruction accuracy and can be effectively embedded in the pre-surgical planning of shoulder arthroplasty or in morphology-based shoulder biomechanics modeling pipelines.


Premorbid shape Posterior model Glenoid bone defect Gaussian processes Total shoulder arthroplasty Pre-surgery planning Musculoskeletal modeling 



We would like to thank the Department of Anatomy at the Faculty of Medicine, CHRU Brest, for making the dry bones available and also the Department of Radiology for scanning the bones. This work was supported by the French State, managed by the National Research Agency with Reference ANR-17-RHUS-0005 and by funding from Institut Carnot and region of Brittany funds.

Supplementary material

10439_2019_2354_MOESM1_ESM.docx (882 kb)
Supplementary material 1 (DOCX 882 kb)


  1. 1.
    Abler, D., S. Berger, A. Terrier, F. Becce, A. Farron, and P. Buchler. A statistical shape model to predict the premorbid glenoid cavity. J. Shoulder Elbow Surg. 27:1800–1808, 2018.CrossRefGoogle Scholar
  2. 2.
    Al Najjar, M., S. S. Mehta, and P. Monga. Three dimensional scapular prints for evaluating glenoid morphology: an exploratory study. J. Clin. Orthop. Trauma 9:230–235, 2018.CrossRefGoogle Scholar
  3. 3.
    Albrecht, T., M. Luthi, T. Gerig, and T. Vetter. Posterior shape models. Med. Image Anal. 17:959–973, 2013.CrossRefGoogle Scholar
  4. 4.
    Bahl, J. S., J. Zhang, B. A. Killen, M. Taylor, L. B. Solomon, J. B. Arnold, D. G. Lloyd, T. F. Besier, and D. Thewlis. Statistical shape modelling versus linear scaling: effects on predictions of hip joint centre location and muscle moment arms in people with hip osteoarthritis. J. Biomech. 85:164–172, 2019.CrossRefGoogle Scholar
  5. 5.
    Besl, P. J., and N. D. McKay. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14:239–256, 1992.CrossRefGoogle Scholar
  6. 6.
    Boileau, P., D. J. Watkinson, A. M. Hatzidakis, and F. Balg. Grammont reverse prosthesis: design, rationale, and biomechanics. J. Shoulder Elbow Surg. 14:147S–161S, 2005.CrossRefGoogle Scholar
  7. 7.
    Borotikar, B., T. Mutsvangwa, V. Burdin, E. Ghorbel, M. Lempereur, S. Brochard, E. Stindel, and C. Roux. Augmented statistical shape modeling for orthopaedic surgery and rehabilitation. In: Medical image analysis and informatics: computer-aided diagnosis and therapy, edited by P. M. D. Azevedo-Marques, A. Mencattini, M. Salmeri, and R. M. Rangayyan. Florida: CRC Press, 2017, pp. 369–426.Google Scholar
  8. 8.
    Brownlee, S., K. Chalkidou, J. Doust, A. G. Elshaug, P. Glasziou, I. Heath, S. Nagpal, V. Saini, D. Srivastava, K. Chalmers, and D. Korenstein. Evidence for overuse of medical services around the world. Lancet 390:156–168, 2017.CrossRefGoogle Scholar
  9. 9.
    Burton, II, W. S., I. Sintini, J. M. Chavarria, J. R. Brownhill, and P. J. Laz. Assessment of scapular morphology and bone quality with statistical models. Comput. Methods Biomech. Biomed. Eng. 22:341–351, 2019.CrossRefGoogle Scholar
  10. 10.
    Cherchi, L., J. F. Ciornohac, J. Godet, P. Clavert, and J. F. Kempf. Critical shoulder angle: measurement reproducibility and correlation with rotator cuff tendon tears. Orthop. Traumatol. Surg. Res. 102:559–562, 2016.CrossRefGoogle Scholar
  11. 11.
    Cignoni, P., M. Callieri, M. Corsini, M. Dellepiane, F. Ganovelli, and G. Ranzuglia. MeshLab: an Open-Source Mesh Processing Tool. In: Eurographics Italian Chapter Conference. Italy, 2008.Google Scholar
  12. 12.
    Daggett, M., B. Werner, P. Collin, M. O. Gauci, J. Chaoui, and G. Walch. Correlation between glenoid inclination and critical shoulder angle: a radiographic and computed tomography study. J. Shoulder Elbow Surg. 24:1948–1953, 2015.CrossRefGoogle Scholar
  13. 13.
    Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26:297–302, 1945.CrossRefGoogle Scholar
  14. 14.
    Dubuisson, M., and A. K. Jain. A modified Hausdorff distance for object matching. In: Proceedings of 12th International Conference on Pattern Recognition, 1994, vol. 561, pp. 566–568.Google Scholar
  15. 15.
    Dwight, T. The range of variation of the human shoulder-blade. Am. Nat. 21:627–638, 1887.CrossRefGoogle Scholar
  16. 16.
    Edwards, T. B. CORR Insights (R): is premorbid glenoid anatomy altered in patients with glenohumeral osteoarthritis? Clin. Orthop. Relat. Res. 471:2940–2941, 2013.CrossRefGoogle Scholar
  17. 17.
    Eraly, K., P. Debeer, I. Jonkers, and J. Vander Sloten. CT-based computerized planning method for shape reconstruction of severe glenoid defects. In: EFORT. Berlin, Germany, 2012.Google Scholar
  18. 18.
    Favard, L., J. Berhouet, G. Walch, J. Chaoui, and C. Levigne. Superior glenoid inclination and glenoid bone loss: definition, assessment, biomechanical consequences, and surgical options. Orthopade 46:1015–1021, 2017.CrossRefGoogle Scholar
  19. 19.
    Frankle, M., S. Siegal, D. Pupello, A. Saleem, M. Mighell, and M. Vasey. The reverse shoulder prosthesis for glenohumeral arthritis associated with severe rotator cuff deficiency. A minimum two-year follow-up study of sixty patients. J. Bone Joint Surg. Am. 87:1697–1705, 2005.Google Scholar
  20. 20.
    Ganapathi, A., J. A. McCarron, X. Chen, and J. P. Iannotti. Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models. J. Shoulder Elbow Surg. 20:234–244, 2011.CrossRefGoogle Scholar
  21. 21.
    Garcia, G. H., J. N. Liu, D. M. Dines, and J. S. Dines. Effect of bone loss in anterior shoulder instability. World J. Orthop. 6:421–433, 2015.CrossRefGoogle Scholar
  22. 22.
    Gelaude, F., T. Clijmans, P. L. Broos, B. Lauwers, and J. Vander Sloten. Computer-aided planning of reconstructive surgery of the innominate bone: automated correction proposals. Comput. Aided Surg. 12:286–294, 2007.CrossRefGoogle Scholar
  23. 23.
    Gumina, S., K. I. Bohsali, and M. A. Wirth. Surgical technique for cuff tear arthropathy. In: Reverse Shoulder Arthroplasty, edited by S. Gumina, F. A. Grassi, and P. Paladini. Switzerland: Springer, 2019, pp. 211–234.CrossRefGoogle Scholar
  24. 24.
    Gupta, A., C. Thussbas, M. Koch, and L. Seebauer. Management of glenoid bone defects with reverse shoulder arthroplasty-surgical technique and clinical outcomes. J. Shoulder Elbow Surg. 27:853–862, 2018.CrossRefGoogle Scholar
  25. 25.
    Hill, J. M., and T. R. Norris. Long-term results of total shoulder arthroplasty following bone-grafting of the glenoid. J. Bone Joint Surg. Am. 83:877–883, 2001.CrossRefGoogle Scholar
  26. 26.
    Hovelius, L., A. Olofsson, B. Sandstrom, B. G. Augustini, L. Krantz, H. Fredin, B. Tillander, U. Skoglund, B. Salomonsson, J. Nowak, and U. Sennerby. Nonoperative treatment of primary anterior shoulder dislocation in patients forty years of age and younger. A prospective twenty-five-year follow-up. J. Bone Joint Surg. Am. 90:945–952, 2008.CrossRefGoogle Scholar
  27. 27.
    Jacq, J. J., C. Schwartz, V. Burdin, R. Gerard, C. Lefevre, C. Roux, and O. Remy-Neris. Building and tracking root shapes. IEEE Trans. Biomed. Eng. 57:696–707, 2010.CrossRefGoogle Scholar
  28. 28.
    Jolliffe, I. Principal Component Analysis. New York: Wiley, 2014.Google Scholar
  29. 29.
    Kandemir, U., R. B. Allaire, J. T. Jolly, R. E. Debski, and P. J. McMahon. The relationship between the orientation of the glenoid and tears of the rotator cuff. J. Bone Joint Surg. Br. 88:1105–1109, 2006.CrossRefGoogle Scholar
  30. 30.
    Klein, S. M., P. Dunning, P. Mulieri, D. Pupello, K. Downes, and M. A. Frankle. Effects of acquired glenoid bone defects on surgical technique and clinical outcomes in reverse shoulder arthroplasty. J. Bone Joint Surg. Am. 92:1144–1154, 2010.CrossRefGoogle Scholar
  31. 31.
    Kocsis, G., D. S. Thyagarajan, K. J. Fairbairn, and W. A. Wallace. A new classification of glenoid bone loss to help plan the implantation of a glenoid component before revision arthroplasty of the shoulder. Bone Joint J. 98:374–380, 2016.CrossRefGoogle Scholar
  32. 32.
    Kontaxis, A., and G. R. Johnson. The biomechanics of reverse anatomy shoulder replacement—a modelling study. Clin. Biomech. 24:254–260, 2009.CrossRefGoogle Scholar
  33. 33.
    Letta, C., A. Schweizer, and P. Furnstahl. Quantification of contralateral differences of the scaphoid: a comparison of bone geometry in three dimensions. Anat. Res. Int. 2014:904275, 2014.Google Scholar
  34. 34.
    Lüthi, M. SCALable image analysis and shape modelling, 2014.Google Scholar
  35. 35.
    Luthi, M., T. Gerig, C. Jud, and T. Vetter. Gaussian process morphable models. IEEE Trans. Pattern Anal. Mach. Intell. 40:1860–1873, 2018.CrossRefGoogle Scholar
  36. 36.
    Malhas, A., A. Rashid, D. Copas, S. Bale, and I. Trail. Glenoid bone loss in primary and revision shoulder arthroplasty. Shoulder Elbow 8:229–240, 2016.CrossRefGoogle Scholar
  37. 37.
    Mayya, M., S. Poltaretskyi, C. Hamitouche, and J. Chaoui. Scapula Statistical Shape Model construction based on watershed segmentation and elastic registration. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, 2013, pp. 101–104.Google Scholar
  38. 38.
    Mayya, M., S. Poltaretskyi, C. Hamitouche, and J. Chaoui. Mesh correspondence improvement using Regional Affine Registration: application to statistical shape model of the scapula. IRBM 36:220–232, 2015.CrossRefGoogle Scholar
  39. 39.
    Mazaheri, P., L. M. Fayad, E. K. Fishman, and S. Demehri. Advanced imaging of the scapula: what every radiologist needs to know. J. Comput. Assist. Tomogr. 40:567–575, 2016.CrossRefGoogle Scholar
  40. 40.
    Merrill, A., K. Guzman, and S. L. Miller. Gender differences in glenoid anatomy: an anatomic study. Surg. Radiol. Anat. 31:183–189, 2009.CrossRefGoogle Scholar
  41. 41.
    Moor, B. K., S. Bouaicha, D. A. Rothenfluh, A. Sukthankar, and C. Gerber. Is there an association between the individual anatomy of the scapula and the development of rotator cuff tears or osteoarthritis of the glenohumeral joint? A radiological study of the critical shoulder angle. Bone Joint J. 95:935–941, 2013.CrossRefGoogle Scholar
  42. 42.
    Mori, D., J. A. Abboud, S. Namdari, and G. R. Williams. Glenoid bone loss in anatomic shoulder arthroplasty: literature review and surgical technique. Orthop. Clin. N. Am. 46:389–397, 2015.CrossRefGoogle Scholar
  43. 43.
    Mutsvangwa, T., V. Burdin, C. Schwartz, and C. Roux. An automated statistical shape model developmental pipeline: application to the human scapula and humerus. IEEE Trans. Biomed. Eng. 62:1098–1107, 2015.CrossRefGoogle Scholar
  44. 44.
    Myronenko, A., and X. Song. Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32:2262–2275, 2010.CrossRefGoogle Scholar
  45. 45.
    Neer, II, C. S. The classic: articular replacement for the humeral head. 1955. Clin. Orthop. Relat. Res. 469:2409–2421, 2011.CrossRefGoogle Scholar
  46. 46.
    Neer, II, C. S., K. C. Watson, and F. J. Stanton. Recent experience in total shoulder replacement. J. Bone Joint Surg. Am. 64:319–337, 1982.CrossRefGoogle Scholar
  47. 47.
    Norris, T. R., and J. P. Iannotti. Functional outcome after shoulder arthroplasty for primary osteoarthritis: a multicenter study. J. Shoulder Elbow Surg. 11:130–135, 2002.CrossRefGoogle Scholar
  48. 48.
    Nyffeler, R. W., and D. C. Meyer. Acromion and glenoid shape: why are they important predictive factors for the future of our shoulders? EFORT Open Rev. 2:141–150, 2017.CrossRefGoogle Scholar
  49. 49.
    Nyffeler, R. W., R. Sheikh, T. S. Atkinson, H. A. Jacob, P. Favre, and C. Gerber. Effects of glenoid component version on humeral head displacement and joint reaction forces: an experimental study. J. Shoulder Elbow Surg. 15:625–629, 2006.CrossRefGoogle Scholar
  50. 50.
    Phipatanakul, W. P., and T. R. Norris. Treatment of glenoid loosening and bone loss due to osteolysis with glenoid bone grafting. J. Shoulder Elbow Surg. 15:84–87, 2006.CrossRefGoogle Scholar
  51. 51.
    Plessers, K., P. Vanden Berghe, C. Van Dijck, R. Wirix-Speetjens, P. Debeer, I. Jonkers, and J. Vander Sloten. Virtual reconstruction of glenoid bone defects using a statistical shape model. J. Shoulder Elbow Surg. 27:160–166, 2018.CrossRefGoogle Scholar
  52. 52.
    Rahmi, H., and A. Jawa. Management of complications after revision shoulder arthroplasty. Curr. Rev. Musculoskelet. Med. 8:98–106, 2015.CrossRefGoogle Scholar
  53. 53.
    Ricchetti, E. T., M. D. Hendel, D. N. Collins, and J. P. Iannotti. Is premorbid glenoid anatomy altered in patients with glenohumeral osteoarthritis? Clin. Orthop. Relat. Res. 471:2932–2939, 2013.CrossRefGoogle Scholar
  54. 54.
    Rouleau, D. M., J. F. Kidder, J. Pons-Villanueva, S. Dynamidis, M. Defranco, and G. Walch. Glenoid version: how to measure it? Validity of different methods in two-dimensional computed tomography scans. J. Shoulder Elbow Surg. 19:1230–1237, 2010.CrossRefGoogle Scholar
  55. 55.
    Salhi, A., V. Burdin, T. Mutsvangwa, S. Sivarasu, S. Brochard, and B. Borotikar. Subject-specific shoulder muscle attachment region prediction using statistical shape models: a validity study. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1640–1643:2017, 2017.Google Scholar
  56. 56.
    Scalise, J. J., M. J. Codsi, J. Bryan, and J. P. Iannotti. The three-dimensional glenoid vault model can estimate normal glenoid version in osteoarthritis. J. Shoulder Elbow Surg. 17:487–491, 2008.CrossRefGoogle Scholar
  57. 57.
    Seidl, A. J., G. R. Williams, and P. Boileau. Challenges in reverse shoulder arthroplasty: addressing glenoid bone loss. Orthopedics 39:14–23, 2016.CrossRefGoogle Scholar
  58. 58.
    Singh, J. A., J. W. Sperling, and R. H. Cofield. Revision surgery following total shoulder arthroplasty: analysis of 2588 shoulders over three decades (1976 to 2008). J. Bone Joint Surg. Br. 93:1513–1517, 2011.CrossRefGoogle Scholar
  59. 59.
    Suwarganda, E. K., L. E. Diamond, D. G. Lloyd, T. F. Besier, J. Zhang, B. A. Killen, T. N. Savage, and D. J. Saxby. Minimal medical imaging can accurately reconstruct geometric bone models for musculoskeletal models. PLoS ONE 14:e0205628, 2019.CrossRefGoogle Scholar
  60. 60.
    Terrier, A., J. Ston, X. Larrea, and A. Farron. Measurements of three-dimensional glenoid erosion when planning the prosthetic replacement of osteoarthritic shoulders. Bone Joint J. 96:513–518, 2014.CrossRefGoogle Scholar
  61. 61.
    Vlachopoulos, L., M. Luthi, F. Carrillo, C. Gerber, G. Szekely, and P. Furnstahl. Restoration of the patient-specific anatomy of the proximal and distal parts of the humerus: statistical shape modeling versus contralateral registration method. J. Bone Joint Surg. Am. 100:e50, 2018.CrossRefGoogle Scholar
  62. 62.
    Walch, G., R. Badet, A. Boulahia, and A. Khoury. Morphologic study of the glenoid in primary glenohumeral osteoarthritis. J. Arthroplasty 14:756–760, 1999.CrossRefGoogle Scholar
  63. 63.
    Walch, G., T. B. Edwards, A. Boulahia, P. Boileau, D. Mole, and P. Adeleine. The influence of glenohumeral prosthetic mismatch on glenoid radiolucent lines: results of a multicenter study. J. Bone Joint Surg. Am. 84:2186–2191, 2002.CrossRefGoogle Scholar
  64. 64.
    Weishaupt, D., M. Zanetti, R. W. Nyffeler, C. Gerber, and J. Hodler. Posterior glenoid rim deficiency in recurrent (atraumatic) posterior shoulder instability. Skeletal Radiol. 29:204–210, 2000.CrossRefGoogle Scholar
  65. 65.
    Yang, Y. M., D. Rueckert, and A. M. Bull. Predicting the shapes of bones at a joint: application to the shoulder. Comput. Methods Biomech. Biomed. Eng. 11:19–30, 2008.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Laboratory for Medical Information Processing (LaTIM)INSERM, UMR1101Brest Cedex - 3France
  2. 2.Department of Image and Information TreatmentIMT AtlantiqueBrestFrance
  3. 3.CHRU de Brest, Hôpital MorvanBrestFrance
  4. 4.Université de Bretagne Occidentale (UBO)BrestFrance
  5. 5.Division of Biomedical EngineeringUniversity of Cape TownCape TownSouth Africa

Personalised recommendations