Segmentation of the right ventricle in four chamber cine cardiac MR images using polar dynamic programming
AuthorRosado-Toro, Jose A.
Altbach, Maria I.
Oliva, Isabel B.
Rodriguez, Jeffrey J.
Avery, Ryan J.
AffiliationUniv Arizona, Dept Elect & Comp Engn
Univ Arizona, Dept Med Imaging
MetadataShow full item record
PublisherPERGAMON-ELSEVIER SCIENCE LTD
CitationJose A. Rosado-Toro, Aiden Abidov, Maria I. Altbach, Isabel B. Oliva, Jeffrey J. Rodriguez, Ryan J. Avery, Segmentation of the right ventricle in four chamber cine cardiac MR images using polar dynamic programming, Computerized Medical Imaging and Graphics, 64, pp 15-25, https://doi.org/10.1016/j.compmedimag.2017.08.002
Rights© 2017 Elsevier Ltd. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThe 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 l's landmarks and traces (i.e., comparing the traces from physician 1 to the semi-automated segmentation using physician l'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. (C) 2017 Elsevier Ltd. All rights reserved.
Note12 month embargo; published online: 18 August 2017
VersionFinal accepted manuscript
SponsorsNational Institute of Health (NIH) [T32-HL007955, HL085385]
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