Right ventricular segmentation in cardiac MRI with moving mesh correspondences

• We propose a semi-automatic segmentation method for right ventricular segmentation.
• The method removes the need for a time-consuming, manually built training set.
• The method does not make prior assumptions as to the intensity distributions or shape.
• The method is flexible for congenital heart disease where the RV undergoes high variations in shape.
• Evaluations over a RV challenge data and HLHS patient data show the robustness of the method.

This study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84 ± 0.11 for the epicardium and 0.79 ± 0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82 ± 0.14, which demonstrates the robustness of the algorithm.