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Automated CT bone segmentation using statistical shape modelling and local template matching

Author

Listed:
  • Elham Taghizadeh
  • Alexandre Terrier
  • Fabio Becce
  • Alain Farron
  • Philippe Büchler

Abstract

Accurate CT bone segmentation is essential to develop chair-side manufacturing of implants based on additive manufacturing. We herewith present an automated method able to accurately segment challenging bone regions, while simultaneously providing anatomical correspondences. The method was evaluated on demanding regions: normal and osteoarthritic scapulae, healthy and atrophied mandibles, and orbital bones. On average, results were accurate with surface distances of approximately 0.5 mm and average Dice coefficients >90%. Since anatomical correspondences are propagated during the segmentation process, this approach can directly yield anatomical measurements, provide design parameters for personalized surgical instruments, or determine the bone geometry to manufacture patient-specific implants.

Suggested Citation

  • Elham Taghizadeh & Alexandre Terrier & Fabio Becce & Alain Farron & Philippe Büchler, 2019. "Automated CT bone segmentation using statistical shape modelling and local template matching," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 22(16), pages 1303-1310, December.
    • Handle: RePEc:taf:gcmbxx:v:22:y:2019:i:16:p:1303-1310
    DOI: 10.1080/10255842.2019.1661391
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