Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI.
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.
EECS Department, College of Engineering, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606, United States. Electronic address: jordan.ringenberg@utoledo.edu.
Jordan
J
Ringenberg
Department of Engineering, Norfolk State University, 700 Park Avenue, Norfolk, VA 23504, United States.
Makarand
M
Deo
EECS Department, College of Engineering, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606, United States.
Vijay
V
Devabhaktuni
Center for Arrhythmia Research, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, United States.
Omer
O
Berenfeld
Interprofessional Immersive Simulation Center, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614, United States.
Pamela
P
Boyers
Interprofessional Immersive Simulation Center, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614, United States.
Jeffrey
J
Gold
D000465:Algorithms; D001185:Artificial Intelligence; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018497:Ventricular Dysfunction, Right / Q000473:pathology*
A priori constraints; Binary difference of Gaussians filter; Cardiac MRI; Optimal thresholding; Ventricular segmentation