Semiautomated segmentation of myocardial contours for fast strain analysis in cine displacement-encoded MRI

Ting Chen*, James Babb, Peter Kellman, Leon Axel, Daniel Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


The purposes of this study were to develop a semiautomated cardiac contour segmentation method for use with cine displacement-encoded MRI and evaluate its accuracy against manual segmentation. This segmentation model was designed with two distinct phases: preparation and evolution. During the model preparation phase, after manual image cropping and then image intensity standardization, the myocardium is separated from the background based on the difference in their intensity distributions, and the endo- and epi-cardial contours are initialized automatically as zeros of an underlying level set function. During the model evolution phase, the model deformation is driven by the minimization of an energy function consisting of Ave terms: model intensity, edge attraction, shape prior, contours interaction, and contour smoothness. The energy function is minimized iteratively by adaptively weighting the Ave terms in the energy function using an annealing algorithm. The validation experiments were performed on a pool of cine data sets of five volunteers. The difference between the semiautomated segmentation and manual segmentation was sufficiently small as to be considered clinically irrelevant. This relatively accurate semiautomated segmentation method can be used to significantly increase the throughput of strain analysis of cine displacement-encoded MR images for clinical applications.

Original languageEnglish (US)
Article number4448996
Pages (from-to)1084-1094
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number8
StatePublished - Aug 2008


  • Energy minimization
  • Magnetic resonance imaging (MRI)
  • Segmentation
  • Strain

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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