Development and evaluation of a semiautomatic segmentation method for the estimation of LV parameters on cine MR images.
The purpose of this study was to develop and evaluate a semiautomatic method for left ventricular (LV) segmentation on cine MR images and subsequent estimation of cardiac parameters. The study group comprised cardiac MR examinations of 18 consecutive patients with known or suspected coronary artery disease. The new method allowed the automatic detection of the LV endocardial and epicardial boundaries on each short-axis cine MR image using a Bayesian flooding segmentation algorithm and weighted least-squares B-splines minimization. Manual editing of the automatic contours could be performed for unsatisfactory segmentation results. The end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF) and LV mass estimated by the new method were compared with the reference values obtained by manually tracing the LV cavity borders. The reproducibility of the new method was determined using data from two independent observers. The mean number of endocardial and epicardial outlines not requiring any manual adjustment was more than 80% and 76% of the total contour number per study, respectively. The mean segmentation time including the required manual corrections was 2.3 +/- 0.7 min per patient. LV volumes estimated by the semiautomatic method were significantly lower than those by manual tracing (P < 0.05), whereas no difference was found for EF and LV mass (P > 0.05). LV indices estimated by the two methods were well correlated (r 0.80). The mean difference between manual and semiautomatic method for estimating EDV, ESV, EF and LV mass was 6.1 +/- 7.2 ml, 3.0 +/- 5.2 ml, -0.6 +/- 4.3% and -6.2 +/- 12.2 g, respectively. The intraobserver and interobserver variability associated with the semiautomatic determination of LV indices was 0.5-1.2% and 0.8-3.9%, respectively. The estimation of LV parameters with the new semiautomatic segmentation method is technically feasible, highly reproducible and time effective.
Mazonakis
Michalis
M
Department of Medical Physics, University of Crete, 71003 Iraklion, Crete, Greece. mazonak@med.uoc.gr
Grinias
Elias
E
Pagonidis
Konstantin
K
Tziritas
George
G
Damilakis
John
J
D000465:Algorithms; D001331:Automation; D001499:Bayes Theorem; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000473:pathology / Q000503:physiopathology; D004699:Endocardium / Q000473:pathology; D005240:Feasibility Studies; D006352:Heart Ventricles / Q000473:pathology* / Q000503:physiopathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D010496:Pericardium / Q000473:pathology; D011336:Probability; D012044:Regression Analysis; D015203:Reproducibility of Results; D012985:Software Design; D013997:Time Factors; D014584:User-Computer Interface