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.
['Ringenberg|Jordan|J|', 'Deo|Makarand|M|', 'Devabhaktuni|Vijay|V|', 'Berenfeld|Omer|O|', 'Boyers|Pamela|P|', 'Gold|Jeffrey|J|']
D000465:Algorithms D001185:Artificial Intelligence D006352:Heart Ventricles D006801:Humans D007089:Image Enhancement D007090:Image Interpretation, Computer-Assisted D019028:Magnetic Resonance Imaging, Cine D010363:Pattern Recognition, Automated D015203:Reproducibility of Results D012680:Sensitivity and Specificity D018497:Ventricular Dysfunction, Right
Cardiac MRI Optimal thresholding A priori constraints Binary difference of Gaussians filter Ventricular segmentation