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Subject-Specific Estimation of Missing Cortical Thickness Maps in Developing Infant Brains

  • Yu Meng
  • Gang Li
  • Yaozong Gao
  • John H. Gilmore
  • Weili Lin
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9601)

Abstract

To accurately chart the dynamic brain developmental trajectories in infants, many longitudinal neuroimaging studies prefer having a complete dataset. Unfortunately, missing data at certain time points are unavoidable in longitudinal datasets. To better use incomplete longitudinal data, we propose a novel method to estimate the subject-specific vertex-wise cortical thickness maps at missing time points, by using a customized regression forest, Dynamically-Assembled Regression Forest (DARF). DARF ensures spatial smoothness of the estimated cortical thickness maps and also the computational efficiency. The proposed method can fully exploit the available information from the subjects both with and without missing scans. Our method has been applied to estimate the missing cortical thickness maps in a longitudinal infant dataset, which includes 31 healthy subjects, with each having up to 5 scans. The experimental results indicate that our method can accurately estimate missing cortical thickness maps, with the average vertex-wise error less than 0.23 mm.

Keywords

Missing data completion Longitudinal cortical thickness Infant brain development 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yu Meng
    • 1
    • 2
  • Gang Li
    • 2
  • Yaozong Gao
    • 1
    • 2
  • John H. Gilmore
    • 2
  • Weili Lin
    • 2
  • Dinggang Shen
    • 2
    Email author
  1. 1.Department of Computer ScienceUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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