Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI

Jordan Ringenberg, Makarand Deo, Vijay Devabhaktuni, Omer Berenfeld, Pamela Boyers, Jeffrey Gold

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

This paper presents a fully automatic method to segment the right ventricle (RV) from short-axis cardiac MRI. A combination of a novel window-constrained accumulator thresholding technique, binary difference of Gaussian (DoG) filters, optimal thresholding, and morphology are utilized to drive the segmentation. A priori segmentation window constraints are incorporated to guide and refine the process, as well as to ensure appropriate area confinement of the segmentation. Training and testing were performed using a combined 48 patient datasets supplied by the organizers of the MICCAI 2012 right ventricle segmentation challenge, allowing for unbiased evaluations and benchmark comparisons. Marked improvements in speed and accuracy over the top existing methods are demonstrated.

Original languageEnglish (US)
Pages (from-to)190-201
Number of pages12
JournalComputerized Medical Imaging and Graphics
Volume38
Issue number3
DOIs
StatePublished - Apr 1 2014

Keywords

  • A priori constraints
  • Binary difference of Gaussians filter
  • Cardiac MRI
  • Optimal thresholding
  • Ventricular segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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