title: Learning-based deformable image registration for infant MR images in the first year of life.
abstract: OBJECTIVE
  Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination.
  
  
  METHODS
  To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration.
  
  
  RESULTS
  We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration results, compared to the state-of-the-art registration methods.
  
  
  CONCLUSIONS
  The proposed learning-based registration method addresses the challenging task of registering infant brain images and achieves higher registration accuracy compared with other counterpart registration methods.
authors.affiliation: School of Information, Linyi University, Linyi, 276005, China.|Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
authors.forename: Shunbo
authors.initials: S
authors.lastname: Hu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.|College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
authors.forename: Lifang
authors.initials: L
authors.lastname: Wei
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
authors.forename: Yanrong
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.|Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000254:growth & development; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007223:Infant; D007231:Infant, Newborn; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging
keywords: deformable image registration; infant brain MR image; regression forest