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Statistical Shape Modeling Approach to Predict Missing Scapular Bone

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

Abstract

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.

Keywords

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

Notes

Acknowledgments

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)

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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

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