TU‐CD‐BRA‐03: Left Ventricle Segmentation with a Coupled Length Regularization and Sparse Shape Prior

Abstract

Purpose:To perform accurate segmentation on left ventricles from Cine MR imagesMethods:We have developed a novel variational segmentation method that incorporates prior knowledge on both geometrical relations and shapes of the endocardium and epicardium. In contrast to conventional approaches that preserve a constant distance between the endo‐ and epicardial contours, we propose to maintain a smoothly varying distance between the endo‐ and epicardium represented by two separate level set functions, which is more robust to shape variations across slices and phases among different patients. The coupled level set representation further allows us to incorporate a sparse composite shape prior to boost the performance. A robust data fidelity of Gaussian mixture is utilized to represent overlapped intensity distributions of each cardiac region under the condition of insufficient gradient information. We evaluated the proposed method on datasets from MICCAI left ventricle segmentation challenge, and compared our method against other state‐of‐the‐art approaches based on Dice similarity coefficient (DSC).Results:Our method successfully segmented all left ventricles in 15 validation datasets from patients of different pathologies. It achieved competitive if not better DSC accuracy compared to other state‐of‐the‐art methods, with mean DSC of 0.89 and standard deviation of 0.04 on endocardium segmentation, and mean DSC of 0.94 and standard deviation of 0.01 on epicardium segmentation.Conclusion:Introducing anatomy‐specific geometric coupling and sparse composite shape priors on endo‐ and epicardium, in addition to length regularization, into the variational method has demonstrated its advantage in the challenging problem of left ventricle segmentation. A joint left and right ventricle segmentation method is under development.