An existing C++ library for efficient segmentation of ultrasound recordings by means of Kalman filtering, the real-time contour tracking library (RCTL), is used as a building block to implement and assess the performance of different Kalman smoothing techniques: fixed-point, fixed-lag, and fixed-interval smoothing. An experimental smoothing technique based on fusion of tracking results and learned mean state estimates at different positions in the heart-cycle is proposed. A set of $29$ recordings with ground-truth left ventricle segmentations provided by a trained medical doctor is used for the performance evaluation.The clinical motivation is to improve the accuracy of automatic left-ventricle tracking, which can be applied to improve the automatic measurement of clinically important parameters such as the ejection fraction. The evaluation shows that none of the smoothing techniques offer significant improvements over regular Kalman filtering. For the Kalman smoothing algorithms, it is argued to be a consequence of the way edge-detection measurements are performed internally in the library. The statistical smoother's lack of improvement is explained by too large interpersonal variations; the mean left-ventricular deformation pattern does not generalize well to individual cases.