Right ventricular segmentation in cardiac MRI with moving mesh correspondences

Abstract

a b s t r a c tThis 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.

A Deep Learning Network for Right Ventricle Segmentation in Short:Axis MRI

“…[11] proposed a novel RV segmentation method based on moving mesh correspondences technology, which can be used as automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) CMR sequences to achieve the RV segmentation in 4D CMR datasets. Besides, the proposed method in [11] can also be extended to be applied in RV congenital heart disease datasets.…”

Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis

“…Other methods demand a strong adjustment of parameters 34,46,51 or are atlas based and, in consequence, computationally expensive and data quantity/quality dependent. 26,28,50 Some authors 31 have used the propagation of a manual segmentation to the rest of the RV, but the dependency on the expert is inevitable and a considerable burden. Other authors 33 have detected the region with maximal motion and selected the RV by a simple threshold.…”

A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

“…For ES, the prior is the union of labels from the previous slices at ES and the label of the current slice at ED. Punithakumar et al [ 139 ] base their segmentation on registration and propagation. A 2D mesh delineating the endocardium or epicardium moves across all phases by establishing point-to-point correspondences.…”