Skip to main content
SpringerLink
Log in

Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration

  • Conference paper
  • First Online:
Information Processing in Medical Imaging (IPMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9123))

Included in the following conference series:

Abstract

This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons require accurate registration with pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. In this work, we use a surface-based registration method to align the MR and CT brain surfaces. The key challenge here is not the registration, but rather the extraction of the cortical surface from the CT image, which includes missing parts of the skull and artifacts introduced by the electrodes. To segment the brain from these images, we propose learning a model of appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Using clinical data, we demonstrate that our method both accurately extracts the brain surface and better localizes electrodes than intensity-based rigid and non-rigid registration methods.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Aharon, M., Elad, M., Bruckstein, A.: K -SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  2. Azarion, A.A., Wu, J., Davis, K.A., Pearce, A., Krish, V.T., Wagenaar, J., Chen, W., Zheng, Y., Wang, H., Lucas, T.H., Litt, B., Gee, J.C.: An open-source automated platform for three-dimensional visualization of subdural electrodes using CT-MRI coregistration. Epilepsia (2014)

    Google Scholar 

  3. Brown, M., Szeliski, R., Winder, S.: Multi-image matching using multi-scale oriented patches. In: CVPR, vol. 1, pp. 510–517 (2005)

    Google Scholar 

  4. Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89(2–3), 114–141 (2003)

    Article  MATH  Google Scholar 

  5. Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  6. do Carmo, M.P.: Differential Geometry of Curves and Surfaces, vol. 2. Prentice-Hall, Englewood Cliffs (1976)

    MATH  Google Scholar 

  7. Hartkens, T., Hill, D., Castellano-Smith, A., Hawkes, D., Maurer, C.R., Martin, A.J., Hall, W., Liu, H., Truwit, C.: Measurement and analysis of brain deformation during neurosurgery. IEEE TMI 22(1), 82–92 (2003)

    Google Scholar 

  8. Hill, D.L., Maurer, C.R.J., Maciunas, R.J., Barwise, J.A., Fitzpatrick, M.J., Wang, M.Y.: Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 3, 514–526 (1998)

    Article  Google Scholar 

  9. Huang, X., Dione, D.P., Compas, C.B., Papademetris, X., Lin, B.A., Bregasi, A., Sinusas, A.J., Staib, L.H., Duncan, J.S.: Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med. Image Anal. 18(2), 253–271 (2014)

    Article  Google Scholar 

  10. Onofrey, J.A., Staib, L.H., Papademetris, X.: Learning nonrigid deformations for constrained multi-modal image registration. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 171–178. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  11. Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S.: Computing 3D Non-rigid brain registration using extended robust point matching for composite multisubject fMRI analysis. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 788–795. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Rubinstein, R., Zibulevsky, M., Elad, M.: Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Technical Report, Technion (2008)

    Google Scholar 

  13. Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE TMI 18(8), 712–721 (1999)

    Google Scholar 

  14. Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)

    Article  Google Scholar 

  15. Spencer, S.S., Sperling, M., Shewmon, A.: Intracranial electrodes. In: Engel Jr., J., Pedley, T.A. (eds.) Epilepsy, a Comprehensive Textbook, pp. 1719–1748. Lippincott-Raven, Philadelphia (1998)

    Google Scholar 

  16. Studholme, C., Hill, D., Hawkes, D.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 32(1), 71–86 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departments of Diagnostic Radiology, Yale University, New Haven, CT, 06520, USA

    John A. Onofrey, Lawrence H. Staib & Xenophon Papademetris

  2. Electrical Engineering, Yale University, New Haven, CT, 06520, USA

    Lawrence H. Staib

  3. Biomedical Engineering, Yale University, New Haven, CT, 06520, USA

    Lawrence H. Staib & Xenophon Papademetris

Authors
  1. John A. Onofrey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lawrence H. Staib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xenophon Papademetris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John A. Onofrey .

Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, University College London, London, United Kingdom

    Sebastien Ourselin

  2. Centre for Medical Image Computing, University College London, London, United Kingdom

    Daniel C. Alexander

  3. Dept. of Radiology, Harvard Medical School Brigham and Women's Hospital, Boston, Massachusetts, USA

    Carl-Fredrik Westin

  4. Centre for Medical Image Computing, University College London, London, United Kingdom

    M. Jorge Cardoso

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Onofrey, J.A., Staib, L.H., Papademetris, X. (2015). Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19992-4_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19991-7

  • Online ISBN: 978-3-319-19992-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation