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Joint Segmentation and Registration for Infant Brain Images

  • Guorong Wu
  • Li Wang
  • John Gilmore
  • Weili Lin
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8848)

Abstract

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical in early brain development studies, which highly relies on the performance of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than the adult brains due to dynamic appearance change with rapid brain development. Fortunately, image segmentation and registration of infant images can assist each other to overcome the above difficulties by harnessing the growth trajectories (temporal correspondences) learned from a large set of training subjects with complete longitudinal data. To this end, we propose a joint segmentation and registration algorithm for infant brain images. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our joint segmentation and registration method in early brain development studies.

Keywords

Training Image Segmentation Result Image Patch Registration Method Registration Result 
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 Switzerland 2014

Authors and Affiliations

  • Guorong Wu
    • 1
  • Li Wang
    • 1
  • John Gilmore
    • 2
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  2. 2.Department of PsychiatryUniversity of North CarolinaChapel HillUSA

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