Nowadays magnetic resonance images (MRI) are being used to calculate important clinical parameters such as ejection fraction (EF), left ventricle myocardium mass (MM), and stroke volume (SV) which are crucial to estimate the cardiac function, surgical planning and create patient-specific heart models, therefore for quantifying accurately these parameters it is also necessary a good delimitation of cardiac structures. The proposed approach presents an automatic segmentation of the left ventricle (LV) and basically is composed by three steps: first, heart structure localization with template matching technique in coronal and sagittal view that is used to restrict axial analysis. Second, ellipsoidal approximation using the axial projection of previous coarse segmentation. Third, a conventional snake algorithm is performed to refine external myocardium boundaries in axial view. The strategy was evaluated using 100 cardiac MRI volumes provided by the ACDC 2017 MICCAI challenge which is composed of 4 different heart diseases, the strategy had an average Dice Score of 0.79.
Accurate volume quantification in magnetic resonance imaging (MRI) provides an important cardiac indicator in congenital heart diseases and furthermore, it is crucial for any surgical planning of congenital surgery. This paper presents an automatic segmentation of the left ventricle (LV) in congenital heart diseases. The proposed approach is basically the suite of three steps: first, a radial saliency analysis coarsely approximates the myocardium boundary. Second, this boundary is refined by choosing, among the candidate points, those ones that follow the most ellipsoidal closed curve. Third, these points serve as the external energy of a conventional snake that is evolved to approximate the inner myocardium boundary. This method requires a minimum parameterization and demands low computational power, in fact, a whole case is segmented in 80 s. The strategy was evaluated using 10 cardiac MRI volumes of actual congenital diseases provided by the HVSMR 2016 challenges, achieving an average Dice of 0.77.
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