Segmentation of Infant Hippocampus Using Common Feature Representations Learned for Multimodal Longitudinal Data

  • Yanrong Guo
  • Guorong Wu
  • Pew-Thian Yap
  • Valerie Jewells
  • Weili Lin
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)


Aberrant development of the human brain during the first year after birth is known to cause critical implications in later stages of life. In particular, neuropsychiatric disorders, such as attention deficit hyperactivity disorder (ADHD), have been linked with abnormal early development of the hippocampus. Despite its known importance, studying the hippocampus in infant subjects is very challenging due to the significantly smaller brain size, dynamically varying image contrast, and large across-subject variation. In this paper, we present a novel method for effective hippocampus segmentation by using a multi-atlas approach that integrates the complementary multimodal information from longitudinal T1 and T2 MR images. In particular, considering the highly heterogeneous nature of the longitudinal data, we propose to learn their common feature representations by using hierarchical multi-set kernel canonical correlation analysis (CCA). Specifically, we will learn (1) within-time-point common features by projecting different modality features of each time point to its own modality-free common space, and (2) across-time-point common features by mapping all time-point-specific common features to a global common space for all time points. These final features are then employed in patch matching across different modalities and time points for hippocampus segmentation, via label propagation and fusion. Experimental results demonstrate the improved performance of our method over the state-of-the-art methods.


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  1. 1.
    Bartsch, T.: The Clinical Neurobiology of the Hippocampus: An integrative view, vol. 151. OUP, Oxford (2012)CrossRefGoogle Scholar
  2. 2.
    Li, J., Jin, Y., Shi, Y., Dinov, I.D., Wang, D.J., Toga, A.W., Thompson, P.M.: Voxelwise spectral diffusional connectivity and Its applications to alzheimer’s disease and intelligence prediction. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 655–662. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Cohen, D.J.: Developmental Psychopathology, 2nd edn. Developmental Neuroscience, vol. 2. Wiley (2006)Google Scholar
  4. 4.
    Coupé, P., et al.: Patch-based Segmentation using Expert Priors: Application to Hippocampus and Ventricle Segmentation. NeuroImage 54, 940–954 (2011)CrossRefGoogle Scholar
  5. 5.
    Rousseau, F., et al.: A Supervised Patch-Based Approach for Human Brain Labeling. IEEE Trans. Medical Imaging 30, 1852–1862 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Tong, T., et al.: Segmentation of MR Images via Discriminative Dictionary Learning and Sparse Coding: Application to Hippocampus Labeling. NeuroImage 76, 11–23 (2013)CrossRefGoogle Scholar
  7. 7.
    Wang, H., et al.: Multi-Atlas Segmentation with Joint Label Fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35, 611–623 (2013)CrossRefGoogle Scholar
  8. 8.
    Wu, G., et al.: A Generative Probability Model of Joint Label Fusion for Multi-Atlas Based Brain Segmentation. Medical Image Analysis 18, 881–890 (2014)CrossRefGoogle Scholar
  9. 9.
    Hardoon, D.R., et al.: Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Computation 16, 2639–2664 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Arora, R., Livescu, K.: Kernel CCA for Multi-view Acoustic Feature Learning using Articulatory Measurements. In: Proceedings of the MLSLP (2012)Google Scholar
  11. 11.
    Lee, G., et al.: Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer. IEEE Transactions on Medical Imaging 34, 284–297 (2015)CrossRefGoogle Scholar
  12. 12.
    Munoz-Mari, J., et al.: Multiset Kernel CCA for Multitemporal Image Classification. In: MultiTemp 2013, pp. 1–4 (2013)Google Scholar
  13. 13.
    Liao, S., et al.: Sparse Patch-Based Label Propagation for Accurate Prostate Localization in CT Images. IEEE Transactions on Medical Imaging 32, 419–434 (2013)CrossRefGoogle Scholar
  14. 14.
    Vercauteren, T., et al.: Diffeomorphic Demons: Efficient Non-parametric Image Registration. NeuroImage 45, 61–72 (2009)CrossRefGoogle Scholar
  15. 15.
    Liu, J., et al.: SLEP: Sparse Learning with Efficient Projections. Arizona State University (2009),
  16. 16.
    Jenkinson, M., et al.: Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage 17, 825–841 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yanrong Guo
    • 1
  • Guorong Wu
    • 1
  • Pew-Thian Yap
    • 1
  • Valerie Jewells
    • 2
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of RadiologyUniversity of North Carolina at Chapel HillChapel HillUSA

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