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In recent years, volumetric (3D) cardiac ultrasound imaging has become more readily available in daily clinical practice due to the introduction of matrix array transducer technology. To date, quantitative analysis of these data sets typically requires a significant amount of user interaction. Recently, our teams introduced methods that could help in automating this process. On the one hand, an edge detection algorithm in combination with a deformable subdivision surface was presented for automatic segmentation of the LV cavity. A real-time, dynamic implementation of this segmentation approach in combination with a Kalman filter allows tracking the subendocardial boundary throughout the cardiac cycle. This method is referred to as RCTL. On the other hand, an automatic 3D motion estimation algorithm was presented in which subsequent image volumes are elastically registered using a B-spline transformation field. This method is called splineMIRIT. Both methods were applied to clinical data to extract relevant functional parameters on global left ventricular (LV) function (i.e. stroke volume (SV) and ejection fraction (EF)). Both methods show a good correlation with the reference method and might thus be used for fully automated estimation of global LV function. Given that RCTL is a fully integrated method (accounting for both segmentation and tracking) it seems to be the better approach towards extracting these parameters. However, whether this remains true when assessing parameters for regional LV function remains to be investigated.