Accurate, robust, and automated longitudinal and cross-sectional brain change analysis

Stephen M. Smith, Yongyue Zhang, Mark Jenkinson, Jacqueline Chen, P. M. Matthews, Antonio Federico, Nicola De Stefano

Research output: Contribution to journalArticlepeer-review

1501 Citations (Scopus)

Abstract

Quantitative measurement of brain size, shape, and temporal change (for example, in order to estimate atrophy) is increasingly important in biomedical image analysis applications. New methods of structural analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method of longitudinal (temporal change) analysis, SIENA, was presented previously. In this paper, improvements to this method are described, and also an extension of SIENA to a new method for cross-sectional (single time point) analysis. The methods are fully automated, robust, and accurate: 0.15% brain volume change error (longitudinal): 0.5-1% brain volume accuracy for single-time point (cross-sectional). A particular advantage is the relative insensitivity to differences in scanning parameters. The methods provide easy manual review of their output by the automatic production of summary images which show the results of the brain extraction, registration, tissue segmentation, and final atrophy estimation.

Original languageEnglish
Pages (from-to)479-489
Number of pages11
JournalNeuroImage
Volume17
Issue number1
DOIs
Publication statusPublished or Issued - Sep 2002
Externally publishedYes

Keywords

  • Atrophy measurement
  • Normalized registration
  • Structural brain analysis

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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