Segmentation of the right ventricle in four chamber cine cardiac MR images using polar dynamic programming. (December 2017)

• Record Type: Journal Article
• Authors: Rosado-Toro, Jose A.; Abidov, Aiden; Altbach, Maria I.; Oliva, Isabel B.; Rodriguez, Jeffrey J.; Avery, Ryan J.
• Is Part Of: Computerized medical imaging and graphics. Volume 62(2017)
• Page Start: 15
• Page End: 25
• Publication Date: 2017-12
• Subjects: Four chamber right ventricle segmentation -- Polar variance -- Polar dynamic programming; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2017.08.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Graphical abstract: Highlights: A semi-automated technique for outlining the RV in 4-CH MR images is described. The technique propagates user-generated throughout cardiac cycle. A novel PDP technique is used for outlining the right ventricular wall. Mutual overlap and fractional area change results where within user-variability. Technique took on average 2 min per patient. Abstract: 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.