Segmentation of the left ventricle (LV) in 3D echocardiography is essential to evaluate cardiac function. It is however a challenging task due to the anisotropy of speckle structure and typical artifacts associated with echocardiography. Several methods have been designed to segment the LV in 3D echocardiograms, but the development of more robust algorithms is still actively investigated. In this paper, we propose a new framework combining Structured Random Forests (SRF), a machine learning technique that shows great potential for edge detection, with Active Shape Models and we compare our segmentation results with state-of-the-art algorithms. We have tested our algorithm on the multi-center, multi-vendor CETUS challenge database, consisting of 45 sequences of 3D echocardiographic volumes. Segmentation was performed and evaluated for end-diastolic (ED) and end-systolic (ES) phases. The results show that combining machine learning with a shape model provides a very competitive LV segmentation, with a mean surface distance of 2.04 ± 0.48 mm for ED and 2.18 ± 0.79 mm for ES. The ejection fraction correlation coefficient reaches 0.87. The overall segmentation score outperforms the best results obtained during the challenge, while there is still room for further improvement, e.g. by increasing the size of the training set for the SRF or by implementing an automatic method to initialize our segmentation.