Spatiotemporal segmentation in echocardiographic image sequences is discussed. Spatial properties and temporal properties are combined to compute segmentation and tracking in a single process. The Markov random field (MRF) framework is used for modeling the energy function. Starting from a reference image, where a manual segmentation is made, a method is developed to estimate the model parameters. An estimation is a crucial point in MRF models. Thus, given an initial segmentation of the sequence, this approach can segment and track a cardiac cavity during the cardiac cycle. Its performance is demonstrated on a real echocardiographic sequence.