Segmentation of time series of 3D cardiac images is clinically clinically used for the assessment of the mechanical function of the left ventricle. To take into account the 4D (3D+T) nature of those images, we propose to extend the deformable surface framework by introducing time-dependent constraints. Thus, in addition to computing an internal force for enforcing the regularity of the deformable model, prior motion knowledge is introduced in the deformation process through either temporal smoothing or trajectory constraints. In this paper, deformable surfaces are represented as simplex meshes owing to their generality and their ability to compute mean curvature at each vertex. The segmentation accuracy of this 4D deformable model is estimated on synthetic SPECT image sequences for which a ground truth about the LV volume is known. Segmentation of non-synthetic SPECT and other modalities 4D images is also discussed.