Segmentation of the left ventricle in cardiac MR images using graph cuts with parametric shape priors

The left ventricle in MR images presents many challenges for automated segmentation including poor contrast at desired tissue boundaries. Segmentation methods based on information from the image alone do not work well in such cases and additional constraints are necessary. In this paper, we propose a novel segmentation method that incorporates parametric shape priors, which do not require statistical training, to the graph cuts technique for robust and efficient segmentations of the left ventricle in cardiac images. We introduce novel terms accounting for shape prior/segmentation and shape prior/image fit to the graph cuts representation. The latter prevents a vicious cycle of bad segmentation/shape priors. We demonstrate the effectiveness of our method on real cardiac images with ground truth segmentations.