Learning-based deformable registration for infant MRI by integrating random forest with auto-context model.

Journal: Medical physics
Published Date: Oct 19, 2017

Abstract

PURPOSE: Accurately analyzing the rapid structural evolution of human brain in the first year of life is a key step in early brain development studies, which requires accurate deformable image registration. However, due to (a) dynamic appearance and (b) large anatomical changes, very few methods in the literature can work well for the registration of two infant brain MR images acquired at two arbitrary development phases, such as birth and one-year-old.

Authors

  • Lifang Wei
    Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
  • Xiaohuan Cao
    School of Automation, Northwestern Polytechnical University, Xi'an, China.
  • Zhensong Wang
    School of Automation Engineering, University of Electronic Science and Technology, Chengdu, 611731, China.
  • Yaozong Gao
  • Shunbo Hu
    School of Information, Linyi University, Linyi, 276005, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Guorong Wu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.

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