The purpose of this study was to evaluate the performance of a semiautomatic segmentation method for the anatomical and functional assessment of both ventricles from cardiac cine magnetic resonance (MR) examinations, reducing user interaction to a "mouse-click". Fifty-two patients with cardiovascular diseases were examined using a 1.5-T MR imaging unit. Several parameters of both ventricles, such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF), were quantified by an experienced operator using the conventional method based on manually-defined contours, as the standard of reference; and a novel semiautomatic segmentation method based on edge detection, iterative thresholding and region growing techniques, for evaluation purposes. No statistically significant differences were found between the two measurement values obtained for each parameter (p > 0.05). Correlation to estimate right ventricular function was good (r > 0.8) and turned out to be excellent (r > 0.9) for the left ventricle (LV). Bland-Altman plots revealed acceptable limits of agreement between the two methods (95%). Our study findings indicate that the proposed technique allows a fast and accurate assessment of both ventricles. However, further improvements are needed to equal results achieved for the right ventricle (RV) using the conventional methodology.

Poster: SERAM 2012 / S-0223 / Segmentacion biventricular automatica del eje corto en resonancia magnetica cardiaca by: M. Souto , L. R. Masip, P. G. Tahoces, J. J. Suarez-Cuenca, A. Martinez, J. M. Carreira; Santiago de Compostela/ES

Ventricular function is a primary indicator for the diagnosis and treatment monitoring of many cardiovascular diseases. Cardiac cine magnetic resonance imaging (MRI) with steady state free precession (SSFP) sequences is regarded to be the standard of reference for the assessment of ventricular function. However, manual segmentation of MRI data is a time consuming process and also suffers from inter/intra-observer variability. This justifies the development of more automated segmentation methods to reduce the amount of time and effort that an experienced operator must spend on this process, and to make such methods practical.

Twenty patients with cardiovascular diseases were examined using a 1.5-T magnetic resonance imaging (MRI) unit, and several parameters of both ventricles, such as ejection fraction (EF), end-diastolic and end-systolic volumes (EDV and ESV, respectively), were quantified by an experienced operator using two methods: 1) our semiautomatic segmentation method based on edge detection, iterative thresholding and region growing techniques, and 2) a commercially available software package based on manual contour tracing.