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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
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.
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.
Albrecht, T., M. Luthi, T. Gerig, and T. Vetter. Posterior shape models. Med. Image Anal. 17:959–973, 2013.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26:297–302, 1945.
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.
Dwight, T. The range of variation of the human shoulder-blade. Am. Nat. 21:627–638, 1887.
Edwards, T. B. CORR Insights (R): is premorbid glenoid anatomy altered in patients with glenohumeral osteoarthritis? Clin. Orthop. Relat. Res. 471:2940–2941, 2013.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Jolliffe, I. Principal Component Analysis. New York: Wiley, 2014.
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.
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.
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.
Kontaxis, A., and G. R. Johnson. The biomechanics of reverse anatomy shoulder replacement—a modelling study. Clin. Biomech. 24:254–260, 2009.
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.
Lüthi, M. SCALable image analysis and shape modelling, 2014.
Luthi, M., T. Gerig, C. Jud, and T. Vetter. Gaussian process morphable models. IEEE Trans. Pattern Anal. Mach. Intell. 40:1860–1873, 2018.
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.
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.
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.
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.
Merrill, A., K. Guzman, and S. L. Miller. Gender differences in glenoid anatomy: an anatomic study. Surg. Radiol. Anat. 31:183–189, 2009.
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.
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.
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.
Myronenko, A., and X. Song. Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32:2262–2275, 2010.
Neer, II, C. S. The classic: articular replacement for the humeral head. 1955. Clin. Orthop. Relat. Res. 469:2409–2421, 2011.
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.
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.
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.
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.
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.
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.
Rahmi, H., and A. Jawa. Management of complications after revision shoulder arthroplasty. Curr. Rev. Musculoskelet. Med. 8:98–106, 2015.
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.
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.
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.
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.
Seidl, A. J., G. R. Williams, and P. Boileau. Challenges in reverse shoulder arthroplasty: addressing glenoid bone loss. Orthopedics 39:14–23, 2016.
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.
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.
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.
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.
Walch, G., R. Badet, A. Boulahia, and A. Khoury. Morphologic study of the glenoid in primary glenohumeral osteoarthritis. J. Arthroplasty 14:756–760, 1999.
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.
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.
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.
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.
Associate Editor Peter E. McHugh oversaw the review of this article.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Salhi, A., Burdin, V., Boutillon, A. et al. Statistical Shape Modeling Approach to Predict Missing Scapular Bone. Ann Biomed Eng 48, 367–379 (2020). https://doi.org/10.1007/s10439-019-02354-6