Near-automated 3D segmentation of left and right ventricles on magnetic resonance images

Abstract

Quantification of left (LV) and right (RV) ventricular volumes and masses from Cardiac Magnetic Resonance (CMR) images is of prime importance for the clinical assessment of a wide variety of cardiac diseases. Despite over a decade of research aimed at the development of fast and reliable tools for automated endo- and epicardial contours identification, the problem is still open, particularly for the RV as a consequence of its more irregular shape and its higher density of trabeculations. In this study, a novel near-automated technique for the segmentation of LV endo- and epicardial as well as RV endocardial contours is presented. The technique is based on a 3D narrow-band statistical level set and on 2D edge-based level set algorithms. The technique was tested on CMR images acquired at both end-diastolic and end-systolic phases. For performance evaluation, an experienced interpreter manually traced ventricular contours, which were used as reference. A series of quantitative error metrics (e.g. mean absolute distance, MAD) were computed between automatically identified and manually traced contours. The results showed the high accuracy of the proposed technique (MAD: LV Endo = 1.4±0.7 px; RV Endo = 1.6±1.2 px; LV Epi = 1.4±0.6 px), which could thus potentially lead to the implementation of a tool for fast and reliable identification of ventricular contours.