A Spatio-temporal Atlas of the Human Fetal Brain with Application to Tissue Segmentation

  • Piotr A. Habas
  • Kio Kim
  • Francois Rousseau
  • Orit A. Glenn
  • A. James Barkovich
  • Colin Studholme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5761)

Abstract

Modeling and analysis of MR images of the early developing human brain is a challenge because of the transient nature of different tissue classes during brain growth. To address this issue, a statistical model that can capture the spatial variation of structures over time is needed. Here, we present an approach to building a spatio-temporal model of tissue distribution in the developing brain which can incorporate both developed tissues as well as transient tissue classes such as the germinal matrix by using constrained higher order polynomial models. This spatio-temporal model is created from a set of manual segmentations through groupwise registration and voxelwise non-linear modeling of tissue class membership, that allows us to represent the appearance as well as disappearance of the transient brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific tissue probability maps and use them to initialize an EM segmentation of the fetal brain tissues. The approach is evaluated using clinical MR images of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Results indicate improvement in performance of atlas-based EM segmentation provided by higher order temporal models that capture the variation of tissue occurrence over time.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Prastawa, M., Gilmore, J.H., Lin, W., Gerig, G.: Automatic segmentation of MR images of the developing newborn brain. Med. Image Anal. 9(5), 457–466 (2005)CrossRefGoogle Scholar
  2. 2.
    Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., Edwards, A.D., Rueckert, D., Hajnal, J.V.: Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 38(3), 461–477 (2007)CrossRefGoogle Scholar
  3. 3.
    Murgasova, M., Dyet, L., Edwards, D., Rutherford, M., Hajnal, J., Rueckert, D.: Segmentation of brain MRI in young children. Acad. Radiol. 14(11), 1350–1366 (2007)CrossRefGoogle Scholar
  4. 4.
    Yoon, U., Fonov, V.S., Perusse, D., Evans, A.C.: The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 45(3), 769–777 (2009)CrossRefGoogle Scholar
  5. 5.
    Studholme, C., Cardenas, V.A., Weiner, M.W.: Multiscale image and multiscale deformation of brain anatomy for building average brain atlases. In: Medical Imaging: Image Processing. In: Proc. SPIE, vol. 4322 (2001)Google Scholar
  6. 6.
    Prayer, D., Kasprian, G., Krampl, E., Ulm, B., Witzani, L., Prayer, L., Brugger, P.C.: MRI of normal fetal brain development. Eur. J. Radiol. 57(2), 199–216 (2006)CrossRefGoogle Scholar
  7. 7.
    Kim, K., Hansen, M.F., Habas, P.A., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Intersection-based registration of slice stacks to form 3D images of the human fetal brain. In: Proc. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1167–1170 (2008)Google Scholar
  8. 8.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit. 32(1), 71–86 (1999)CrossRefGoogle Scholar
  9. 9.
    Pohl, K.M., Fisher, J., Shenton, M., McCarley, R.W., Grimson, W.E.L., Kikinis, R., Wells, W.M.: Logarithm odds maps for shape representation. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 955–963. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 885–896 (1999)CrossRefGoogle Scholar
  11. 11.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 897–908 (1999)CrossRefGoogle Scholar
  12. 12.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Piotr A. Habas
    • 1
    • 2
  • Kio Kim
    • 1
    • 2
  • Francois Rousseau
    • 3
  • Orit A. Glenn
    • 2
  • A. James Barkovich
    • 2
  • Colin Studholme
    • 1
    • 2
  1. 1.Biomedical Image Computing GroupUSA
  2. 2.Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoUSA
  3. 3.LSIIT, UMR CNRS/ULP 7005IllkirchFrance

Personalised recommendations