Three-dimensional left ventricular segmentationfrom magnetic resonance imaging for patient specific modellingpurposes
Enrico G. Caiani ; Andrea Colombo ; Mauro Pepi ; et al. ; - ASI Sponsor
Oct - 2013
DOI: 10.1093/europace/euu232

journal : Europace

Volume : 16 ; Issue : 4
type: Article Journal

Aims To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with ‘gold standard’ manual tracings and discs-summation method.Methods and results The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland–Altman analyses were applied vs. the manual ‘gold standard’. Active shape modelling results showed high correlations with manual values both for LV volumes (r2 > 0.98) and ejection fraction (EF) (r2 > 0.90), non-significant biases and narrow limits of agreement.Conclusion The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling.

keywords : Cardiac magnetic resonance imaging ; Left ventricular volume ; Image segmentation ; Patient-specific modelling ; Active shape models