A parallel watershed-based approach for left ventricular segmentation accurately estimated six cardiac function parameters in a 20-patient MRI database.
A novel parallel watershed-based segmentation approach for cardiac MRI accurately computes six key left ventricular function parameters.
Magnetic resonance imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac magnetic resonance (MR) image's features and the large variability of images among several patients, still influences the cardiac cavities’ segmentation and needs to be carried out. In this study, the authors have profoundly reviewed and fully compared semi‐automated segmentation methods performed on cardiac cine‐MR short‐axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesised works is limited if not null. For the sake of ensuring the highest coverage of the left ventricle parameters computing, they have introduced a parallel watershed‐based approach to segment the left ventricular allowing hence the computation of six parameters (end‐diastolic volume, end‐systolic volume, ejection fraction, cardiac output, stroke volume, and left ventricular mass). An algorithm is associated with the main considered measurements. The experimental results that were obtained through studying 20 patients’ MRI data base demonstrate their approach's accuracy in estimating real values of the parameters’ set thanks to a faithful segmentation of the myocardium.
Mahmoudi et al. (Fri,) conducted a other in Cardiac MRI evaluation (n=20). Parallel watershed-based approach for left ventricular segmentation was evaluated on Accuracy in estimating real values of six cardiac parameters (EDV, ESV, EF, CO, SV, and LV mass). A parallel watershed-based approach for left ventricular segmentation accurately estimated six cardiac function parameters in a 20-patient MRI database.