Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor

Med Image Anal. 2012 Feb;16(2):351-60. doi: 10.1016/j.media.2011.09.002. Epub 2011 Oct 14.

Abstract

This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Animals
  • Computer Simulation
  • Dogs
  • Echocardiography, Three-Dimensional / methods*
  • Heart Ventricles / diagnostic imaging*
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Models, Cardiovascular
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Subtraction Technique