A Hybrid Multishape Learning Framework for Longitudinal Prediction of Cortical Surfaces and Fiber Tracts Using Neonatal Data

  • Islem Rekik
  • Gang Li
  • Pew-Thian Yap
  • Geng Chen
  • Weili Lin
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)

Abstract

Dramatic changes of the human brain during the first year of postnatal development are poorly understood due to their multifold complexity. In this paper, we present the first attempt to jointly predict, using neonatal data, the dynamic growth pattern of brain cortical surfaces (collection of 3D triangular faces) and fiber tracts (collection of 3D lines). These two entities are modeled jointly as a multishape (a set of interlinked shapes). We propose a hybrid learning-based multishape prediction framework that captures both the diffeomorphic evolution of the cortical surfaces and the non-diffeomorphic growth of fiber tracts. In particular, we learn a set of geometric and dynamic cortical features and fiber connectivity features that characterize the relationships between cortical surfaces and fibers at different timepoints (0, 3, 6, and 9 months of age). Given a new neonatal multishape at 0 month of age, we hierarchically predict, at 3, 6 and 9 months, the postnatal cortical surfaces vertex-by-vertex along with fibers connected to adjacent faces to these vertices. This is achieved using a new fiber-to-face metric that quantifies the similarity between multishapes. For validation, we propose several evaluation metrics to thoroughly assess the performance of our framework. The results confirm that our framework yields good prediction accuracy of complex neonatal multishape development within a few seconds.

Keywords

Ground Truth Cortical Surface Fiber Tract Posterior Cingulate Cortex Connectivity Feature 
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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Islem Rekik
    • 1
  • Gang Li
    • 1
  • Pew-Thian Yap
    • 1
  • Geng Chen
    • 1
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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