Segmentation of the right ventricle in four chamber cine cardiac MR images using polar dynamic programming

Jose A. Rosado-Toro, Aiden Abidov, Maria I. Altbach, Isabel B. Oliva, Jeffrey J. Rodriguez, Ryan J. Avery

Research output: Contribution to journalArticle

1 Scopus citations


The four chamber plane is currently underutilized in the right ventricular segmentation community. Four chamber information can be useful to determine ventricular short axis stacks and provide a rough estimate of the right ventricle in short axis stacks. In this study, we develop and test a semi-automated technique for segmenting the right ventricle in four chamber cine cardiac magnetic resonance images. The three techniques that use minimum cost path algorithms were used. The algorithms are: Dijkstra's shortest path algorithm (Dijkstra), an A* algorithm that uses length, curvature and torsion into an active contour model (ALCT), and a variation of polar dynamic programming (PDP). The techniques are evaluated against the expert traces using 175 cardiac images from 7 patients. The evaluation first looks at mutual overlap metrics and then focuses on clinical measures such as fractional area change (FAC). The mean mutual overlap between the physician's traces ranged from 0.85 to 0.88. Using as reference physician 1′s landmarks and traces (i.e., comparing the traces from physician 1 to the semi-automated segmentation using physician 1′s landmarks), the PDP algorithm has a mean mutual overlap of 0.8970 compared to 0.8912 for ALCT and 0.8879 for Dijkstra. The mean mutual overlap between the BP regions generated by physician 1 and physician 2 landmarks are 0.9674, 0.9605 and 0.9531 for PDP, ALCT and Dijkstra, respectively. The FAC correlation coefficient between the physician's traces ranged from 0.73 to 0.93.

Original languageEnglish (US)
Pages (from-to)15-25
Number of pages11
JournalComputerized Medical Imaging and Graphics
StatePublished - Dec 2017



  • Four chamber right ventricle segmentation
  • Polar dynamic programming
  • Polar variance

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

Cite this