Skip to main content

Minimally Supervised Segmentation and Meshing of 3D Intervertebral Discs of the Lumbar Spine for Discectomy Simulation

  • Chapter
  • First Online:
Recent Advances in Computational Methods and Clinical Applications for Spine Imaging

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 20))

  • 1220 Accesses

Abstract

A framework for 3D segmentation of healthy and herniated intervertebral discs from T2-weighted MRI was developed that exploits weak shape priors encoded in simplex mesh active surface models. An ellipsoidal simplex template mesh was initialized within the disc image boundary through affine landmark-based registration, and was allowed to deform according to image gradient forces. Coarse-to-fine multi-resolution approach was adopted in conjunction with decreasing shape memory forces to accurately capture the disc boundary. User intervention is allowed to turn off the shape feature and guide model deformation when internal shape memory influence hinders detection of pathology. For testing, 16 healthy discs were automatically segmented, and 5 pathological discs were segmented with minimal supervision. A resulting surface mesh was utilized for disc compression simulation under gravitational and weight loads and Meshless-Mechanics (MM)-based cutting using Simulation Open Framework Architecture (SOFA). The surface-mesh based segmentation method is part of a processing pipeline for anatomical modeling to support interactive surgery simulation. Segmentation results were validated against expert guided segmentation and demonstrate mean absolute shape distance error of less than 1 mm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.sofa-framework.org/sofa/doc/sofadocumentation.pdf.

  2. 2.

    http://www.sofa-framework.org/classes?show=Triangle-PressureForceField.

References

  1. Luoma, K., et al.: Low back pain in relation to lumbar disc degeneration. Spine 25(4), 487–492 (2000)

    Article  Google Scholar 

  2. Freemont, A.J., et al.: Current understanding of cellular and molecular events in intervertebral disc degeneration: implications for therapy. J. Pathol. 196(4), 374–379 (2002)

    Article  Google Scholar 

  3. An, H., Anderson, P.: Disc degeneration. Spine 29, 2677–2678 (2004)

    Article  Google Scholar 

  4. Evans, A.C., et al.: 3D statistical neuroanatomical models from 305 MRI volumes. In: Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, pp. 1813–1817 (1993)

    Google Scholar 

  5. Prastawa, M., et al.: A brain tumor segmentation framework based on outlier detection. Med. Img. Anal. 8(3), 275–283 (2004)

    Article  Google Scholar 

  6. Michopoulou, S.K., et al.: IEEE Trans. Biomed. Eng. 56(9), 2225–2231 (2009)

    Google Scholar 

  7. Alomari, R.S., et al.: Lumbar spine disc herniation diagnosis with a joint shape model. In: Proceedings of MICCAI Workshop on Computational Spine Imaging (2013)

    Google Scholar 

  8. Klinder, T., et al.: Automated model-based vertebra detection, identification, and segmentation in CT images. Med. Image Anal. 13, 471–482 (2009)

    Article  Google Scholar 

  9. Kelm, M.B., et al.: Spine detection in CT and MR using iterated marginal space learning. Med. Imaging Anal. 17(8), 1283–1292 (2013)

    Article  Google Scholar 

  10. Lalonde, N.M., et al.: Method to geometrically personalize a detailed finite-element model of the spine. IEEE Trans. Biomed. Eng. 60(7), 2014–2021 (2013)

    Article  MathSciNet  Google Scholar 

  11. Neubert, A., et al.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Phys. Med. Biol. 57, 8357–8376 (2012)

    Article  Google Scholar 

  12. Fardon, D.F., Milette, P.C.: Nomenclature and classification of Lumbar disc pathology. Spine 26(5), E93–E113 (2001)

    Article  Google Scholar 

  13. Perona, P., et al.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), pp. 629–639 (1990)

    Google Scholar 

  14. Niadich, T.P., et al.: Imaging of the Spine. Elsevier, Philadelphia (2010)

    Google Scholar 

  15. Insight Segmentation and Registration Toolkit. www.itk.org

  16. Delingette, H.: General object reconstruction based on simplex meshes. Intl. J. Comp. Vis. 32(2), 111–146 (1999)

    Article  Google Scholar 

  17. Gilles, B., et al.: Muskuloskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med. Img. Anal. 14(3), 291–302 (2010)

    Article  MathSciNet  Google Scholar 

  18. Tejos, C., et al.: Simplex mesh diffusion snakes: Integrating 2d and 3d deformable models and statistical shape knowledge in a variational framework. Intl. J. Comp. Vis. 85(1), 19–34 (2009)

    Article  Google Scholar 

  19. Faure, F., et al.: Soft Tissue Biomechanical Modeling for Computer Assisted Surgery, pp. 283–321. Springer, Heidelberg (2012)

    Google Scholar 

  20. Labelle, F., Shewchuk, J.R.: Isosurface stuffing: fast tetrahedral meshes with good dihedral angles. ACM. Trans. Gr. 26(3), 57 (2007)

    Article  Google Scholar 

  21. Malandrino, A., et al.: The effect of sustained compression on Oxygen metabolic transport in the intervertebral disc decreases with degenerative changes. PLoS Comput. Biol. 7(8), e1002112 (2011)

    Article  Google Scholar 

  22. Spilker, R.L.: Mechanical behavior of a simple model of an intervertebral disc under compressive loading. J. Biomech. 13, 895–901 (1980)

    Article  Google Scholar 

  23. Barbieri, E., et al.: A new weight-function enrichment in meshless methods for multiple cracks in linear elasticity. Intl. J. Num. Meth. Eng. 90(2), 177–195 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  24. Aras, R., Shen, Y., Audette, M.: Point-based methods for medical modeling and simulation. In: MODSIM World conference 2014 (2014)

    Google Scholar 

  25. Aras, R.: Meshless mechanics and point-based visualization methods for surgical simulations. Ph.D Dissertation, Old Dominion University (2014)

    Google Scholar 

  26. Mesh Valmet: Validation Metric for Meshes. http://www.nitrc.org/projects/meshvalmet/

  27. Simulation Open Framework Architecture. http://www.sofa-framework.org/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rabia Haq .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Haq, R., Aras, R., Besachio, D.A., Borgie, R.C., Audette, M.A. (2015). Minimally Supervised Segmentation and Meshing of 3D Intervertebral Discs of the Lumbar Spine for Discectomy Simulation. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-14148-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14148-0_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14147-3

  • Online ISBN: 978-3-319-14148-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics