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Abstract

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration for clinical use, however, is challenging since standard registration techniques often fail due to poor initial alignment. The main causes of registration failure are the small overlap between scans which focus on different parts of the spine and/or substantial change in shape (e.g. after correction of abnormal curvature) and appearance (e.g. due to surgical implants). To overcome these issues we propose a registration approach which incorporates estimates of vertebrae locations obtained from a learning-based classification method. These location priors are used to initialize the registration and to provide semantic information within the optimization process. Quantitative evaluation on a database of 93 patients with a total of 276 registrations on longitudinal spine CT demonstrate that our registration method significantly reduces the number of failure cases.

Keywords

Image Registration Semantic Information Registration Method Registration Error Location Prior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Jarvik, J.J., Hollingworth, W., Heagerty, P., Haynor, D.R., Deyo, R.A.: The longitudinal assessment of imaging and disability of the back (LAIDBack) study: baseline data. Spine 26(10), 1158–1166 (2001)CrossRefGoogle Scholar
  2. 2.
    Johnson, H.J., Christensen, G.E.: Consistent landmark and intensity-based image registration. TMI 21(5), 450–461 (2002)Google Scholar
  3. 3.
    Studholme, C., Hill, D., Hawkes, D.: Incorporating connected region labelling into automated image registration using mutual information. In: Mathematical Methods in Biomedical Image Analysis, pp. 23–31 (1996)Google Scholar
  4. 4.
    Konukoglu, E., Criminisi, A., Pathak, S., Robertson, D., White, S., Haynor, D., Siddiqui, K.: Robust linear registration of CT images using random regression forests. In: SPIE Medical Imaging (2011)Google Scholar
  5. 5.
    Russakoff, D.B., Rohlfing, T., Adler Jr., J.R., Maurer Jr., C.R.: Intensity-based 2D-3D spine image registration incorporating a single fiducial marker. Academic Radiology 12(1), 37–50 (2005)CrossRefGoogle Scholar
  6. 6.
    Hu, Y., Haynor, D.R.: Multirigid registration of MR and CT images of the cervical spine. In: SPIE Medical Imaging (2004)Google Scholar
  7. 7.
    Cech, P., Andronache, A., Wang, L., Szekely, G., Cattin, P.: Piecewise rigid multimodal spine registration. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung fuer die Medizin 2006, pp. 211–215. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Li, W., Sode, M., Saeed, I., Lang, T.: Automated registration of hip and spine for longitudinal QCT studies: integration with 3D densitometric and structural analysis. Bone 38(2), 273–279 (2006)CrossRefGoogle Scholar
  9. 9.
    Steger, S., Wesarg, S.: Automated Skeleton Based Multi-modal Deformable Registration of Head&Neck Datasets. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 66–73. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. MedIA 13(3), 471–482 (2009)Google Scholar
  11. 11.
    Huang, S.H., Chu, Y.H., Lai, S.H., Novak, C.L.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. TMI 28(10), 1595–1605 (2009)Google Scholar
  12. 12.
    Glocker, B., Zikic, D., Konukoglu, E., Haynor, D., Criminisi, A.: Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Schmidt, S., Kappes, J., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., Schnörr, C.: Spine detection and labeling using a parts-based graphical model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Oktay, A., Akgul, Y.: Localization of the Lumbar discs using machine learning and exact probabilistic inference. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 158–165. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. MedIA 12(6), 731–741 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ben Glocker
    • 1
  • Darko Zikic
    • 2
  • David R. Haynor
    • 3
  1. 1.Biomedical Image Analysis GroupImperial College LondonUK
  2. 2.Microsoft ResearchCambridgeUK
  3. 3.University of WashingtonSeattleUSA

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