We have recently introduced a novel framework to efficiently deal with 3D segmentation of challenging inhomogeneous data in real-time. However, the existing framework still relied on manual initialization, which prevented taking full advantage of the computational speed of the method. In the present manuscript we propose an automatic initialization scheme adapted to 3D echocardiographic data and we couple it with the existing segmentation framework. Moreover, a novel segmentation functional, which explicitly takes the darker appearance of the blood into account, is also proposed in the present manuscript. We show that fully automatic segmentation of the left ventricle using the proposed method provides an efficient, fast and accurate solution for quantification of the main cardiac indices used in routine clinical practice.