Quantification of left ventricular (LV) size and function from cardiac magnetic resonance (CMR) images requires manual tracing of LV borders on multiple 2D slices, which is subjective, tedious and time-consuming experience. This paper presents a fully automated method for endocardial and epicardial boundaries detection for the assessment of LV volumes, ejection fraction (EF) and mass from CMR images. The segmentation procedure is based on a combined level set approach initialized by an automatically detected point inside the LV cavity. To validate the proposed technique, myocardial boundaries were manually traced on end-diastolic (ED) and end-systolic (ES) frames by an experienced cardiologist. Bland-Altman analysis and linear regression were used to validate LV volumes, EF and mass and similarity metrics were applied to assess the agreement between manually and automatically detected contours. We found minimal biases and narrow limits of agreement for LV volumes, EF and mass; Dice coefficient, Jaccard index and Hausdorff distance evaluated for 2D ED and ES endocardial and epicardial boundaries showed adequate overlapping. The proposed technique allows fast and accurate assessment of LV volumes, EF and mass as a basis for accurate quantification of LV size and function, and myocardial scar from CMR images.

Fully automated assessment of left ventricular volumes and mass from cardiac magnetic resonance images / Marino, M.; Veronesi, F.; Corsi, C.. - STAMPA. - 2014:(2014), pp. 6943781.1079-6943781.1082. (Intervento presentato al convegno 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 tenutosi a usa nel 2014) [10.1109/EMBC.2014.6943781].

Fully automated assessment of left ventricular volumes and mass from cardiac magnetic resonance images

VERONESI, FEDERICO;CORSI, CRISTIANA
2014

Abstract

Quantification of left ventricular (LV) size and function from cardiac magnetic resonance (CMR) images requires manual tracing of LV borders on multiple 2D slices, which is subjective, tedious and time-consuming experience. This paper presents a fully automated method for endocardial and epicardial boundaries detection for the assessment of LV volumes, ejection fraction (EF) and mass from CMR images. The segmentation procedure is based on a combined level set approach initialized by an automatically detected point inside the LV cavity. To validate the proposed technique, myocardial boundaries were manually traced on end-diastolic (ED) and end-systolic (ES) frames by an experienced cardiologist. Bland-Altman analysis and linear regression were used to validate LV volumes, EF and mass and similarity metrics were applied to assess the agreement between manually and automatically detected contours. We found minimal biases and narrow limits of agreement for LV volumes, EF and mass; Dice coefficient, Jaccard index and Hausdorff distance evaluated for 2D ED and ES endocardial and epicardial boundaries showed adequate overlapping. The proposed technique allows fast and accurate assessment of LV volumes, EF and mass as a basis for accurate quantification of LV size and function, and myocardial scar from CMR images.
2014
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
1079
1082
Fully automated assessment of left ventricular volumes and mass from cardiac magnetic resonance images / Marino, M.; Veronesi, F.; Corsi, C.. - STAMPA. - 2014:(2014), pp. 6943781.1079-6943781.1082. (Intervento presentato al convegno 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 tenutosi a usa nel 2014) [10.1109/EMBC.2014.6943781].
Marino, M.; Veronesi, F.; Corsi, C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/555026
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