Elastic Demons: Characterizing Cortical Development in Neonates Using an Implicit Surface Registration

  • Paul C. Pearlman
  • Ivana Išgum
  • Karina J. Kersbergen
  • Manon J. N. L. Benders
  • Max A. Viergever
  • Josien P. W. Pluim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7570)


We present an approach for nonrigid registration of consecutive neonatal cortical surfaces from MR images acquired at 30 and 40 week corrected gestational ages. Surfaces are registered implicitly using a method based on the Demons algorithm. Our key innovation is removing the Gaussian smoothing term in Demons in favor of an elasticity constraint that simultaneously promotes more realistic deformations and smooths the deformation field. This is advantageous because the constraint smooths the deformation field along the surface rather than across it. Therefore, fine deformations, such as those necessary to characterize small, new cortical folds, are preserved. The estimated deformation fields are then used to characterize brain development.


Cortical Development Nonrigid Registration Gaussian Smoothing Deformable Image Registration Realistic Deformation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Awate, S., Yushkevich, P., Song, Z., Licht, D., Gee, J.: Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development. Neuroimage 53(2), 450–459 (2010)CrossRefGoogle Scholar
  2. 2.
    Dubois, J., Benders, M., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter, R., Sizonenko, S.V., Borradori-Tolsa, C., Mangin, J.F., Hüppi, P.S.: Mapping the early cortical folding process in the preterm newborn brain. Cerebral Cortex 18, 1444–1454 (2008)CrossRefGoogle Scholar
  3. 3.
    Nordahl, C.W., Dierker, D., Mostafavi, I., Schumann, C.M., Rivera, S.M., Amaral, D.G., van Essen, D.C.: Cortical folding abnormalities in autism revealed by surface-based morphometry. Journal of Neuroscience 27(43), 11725–11735 (2007)CrossRefGoogle Scholar
  4. 4.
    Yu, P., Grant, P., Qi, Y., Han, X., Segonne, F., Pienaar, R., Busa, E., Pacheco, J., Makris, N., Buckner, R., Golland, P., Fischl, B.: Cortical surface shape analysis based on spherical wavelets. IEEE Transactions on Medical Imaging 26(4), 582–597 (2007)CrossRefGoogle Scholar
  5. 5.
    Thomas Yeo, B.T., Yu, P., Grant, P.E., Fischl, B., Golland, P.: Shape Analysis with Overcomplete Spherical Wavelets. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 468–476. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Rodriguez-Carranza, C.E., Mukherjee, P., Vigneron, D., Barkovich, J., Studholme, C.: A framework for in-vivo quantification of regional brain folding in premature neonates. Neuroimage 41(2), 462–478 (2008)CrossRefGoogle Scholar
  7. 7.
    Pienaar, R., Fischl, B., Caviness, V., Makris, N., Grant, P.E.: A methodology for analyzing curvature in the developing brain from preterm to adult. International Journal of Imaging Systems Technology 18, 42–68 (2008)CrossRefGoogle Scholar
  8. 8.
    Aljabar, P., Bhatia, K., Murgasova, M., Hajnal, J., Boardman, J., Srinivasan, L., Rutherford, M., Dyet, L., Edwards, A., Rueckert, D.: Assessment of brain growth in early childhood using deformation-based morphometry. Neuroimage 39(1), 348–358 (2008)CrossRefGoogle Scholar
  9. 9.
    Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S., Rutherford, M., Edwards, A., Hajnal, J., Rueckert, D.: A combined manifold learning analysis of shape and appearance to characterize neonatal brain development. IEEE Transactions on Medical Imaging 30(12), 2072–2086 (2011)CrossRefGoogle Scholar
  10. 10.
    Huang, X., Paragios, N., Metaxas, D.: Shape registration in implicit spaces using information theory and free form deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(8), 1303–1318 (2006)CrossRefGoogle Scholar
  11. 11.
    Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: ilogdemons: A demons-based registration algorithm for tracking incompressible elastic biological tissues. International Journal of Computer Vision 92(1), 92–111 (2011)CrossRefGoogle Scholar
  12. 12.
    Kwan, R.S., Evans, A., Pike, G.: Mri simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging 18(11), 1085–1097 (1999)CrossRefGoogle Scholar
  13. 13.
    Shattuck, D.W., Prasad, G., Mirza, M., Narr, K.L., Toga, A.W.: Online resource for validation of brain segmentation methods. NeuroImage 45(2), 431–439 (2009)CrossRefGoogle Scholar
  14. 14.
    Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  15. 15.
    Wang, H., Dong, L., O’Daniel, J., Mohan, R., Garden, A.S., Ang, K.K., Kuban, D.A., Bonnen, M., Chang, J.Y., Cheung, R.: Validation of an accelerated ’demons’ algorithm for deformable image registration in radiation therapy. Physics in Medicine and Biology 50(12), 2887–2905 (2005)CrossRefGoogle Scholar
  16. 16.
    Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)CrossRefGoogle Scholar
  17. 17.
    Lüthi, M., Albrecht, T., Vetter, T.: Curvature guided surface registration using level sets. In: Proceedings of CARS, pp. 126–128 (2007)Google Scholar
  18. 18.
    van Essen, D.: A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 23, 313–318 (1997)CrossRefGoogle Scholar
  19. 19.
    Toro, R., Burnod, Y.: A morphogenetic model for the development of cortical convolutions. Cerebral Cortex 15, 1900–1913 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paul C. Pearlman
    • 1
  • Ivana Išgum
    • 1
  • Karina J. Kersbergen
    • 2
  • Manon J. N. L. Benders
    • 2
  • Max A. Viergever
    • 1
  • Josien P. W. Pluim
    • 1
  1. 1.Image Sciences InstituteThe Netherlands
  2. 2.Department of NeonatalogyUniversity Medical Center UtrechtUtrechtThe Netherlands

Personalised recommendations