Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms

Zhong Xue, Dinggang Shen, Bilge Karacali, Joshua Stern, David Rottenberg, Christos Davatzikos

Research output: Contribution to journalArticle

73 Citations (Scopus)

Abstract

Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.

Original languageEnglish
Pages (from-to)855-866
Number of pages12
JournalNeuroImage
Volume33
Issue number3
DOIs
Publication statusPublished - 2006 Nov 15
Externally publishedYes

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ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

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