Prediction of Infant MRI Appearance and Anatomical Structure Evolution Using Sparse Patch-Based Metamorphosis Learning Framework

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9467)


Magnetic resonance imaging (MRI) of pediatric brain provides invaluable information for early normal and abnormal brain development. Longitudinal neuroimaging has spanned various research works on examining infant brain development patterns. However, studies on predicting postnatal brain image evolution remain scarce, which is very challenging due to the dynamic tissue contrast change and even inversion in postnatal brains. In this paper, we unprecedentedly propose a dual image intensity and anatomical structure (label) prediction framework that nicely links the geodesic image metamorphosis model with sparse patch-based image representation, thereby defining spatiotemporal metamorphic patches encoding both image photometric and geometric deformation. In the training stage, we learn the 4D metamorphosis trajectories for each training subject. In the prediction stage, we define various strategies to sparsely represent each patch in the testing image using the training metamorphosis patches; while progressively incrementing the richness of the patch (from appearance-based to multimodal kinetic patches). We used the proposed framework to predict 6, 9 and 12-month brain MR image intensity and structure (white and gray matter maps) from 3 months in 10 infants. The proposed framework showed promising preliminary prediction results for the spatiotemporally complex, drastically changing brain images.


Baseline Image Training Subject Intensity Prediction Early Brain Development Multimodal Feature 
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  1. 1.
    Knickmeyer, R., Gouttard, S., Kang, C., Evans, D., Wilber, K., Smith, J., Hamer, R., Lin, W., Gerig, G., Gilmore, J.: A structural mri study of human brain development from birth to 2 years. J. Neurosci. 28, 12176–12182 (2008)CrossRefGoogle Scholar
  2. 2.
    Fletcher, P.: Geodesic regression and the theory of least squares on riemannian manifolds. Int. J. Comput. Vis. 105, 171–185 (2013)zbMATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Niethammer, M., Huang, Y., Vialard, F.-X.: Geodesic regression for image time-series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Trouvé, A., Younes, L.: Metamorphoses through lie group action. Found. Comput. Math. 5, 173–198 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Wu, G., Kim, M., Sanroma, G., Wang, Q., Munsell, B., Shen, D.: Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. Neuroimage 106, 34–46 (2015)CrossRefGoogle Scholar
  6. 6.
    Shi, F., Wang, L., Wu, G., Li, G., Gilmore, J., Lin, W., Shen, D.: Neonatal atlas construction using sparse representation. Hum. Brain Mapp. 35, 4663–4677 (2014)CrossRefGoogle Scholar
  7. 7.
    Beg, M., Miller, M., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61, 139–157 (2005)CrossRefGoogle Scholar
  8. 8.
    Garcin, L., Younes, L.: Geodesic image matching: a wavelet based energy minimization scheme. In: Rangarajan, A., Vemuri, B.C., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 349–364. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, Q., Kim, M., Shi, Y., Wu, G., Shen, D.: Predict brain MR image registration via sparse learning of appearance and transformation. Med. Image Anal. 20, 61–75 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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