This paper describes a segmentation method for time series of 3D cardiac images based on deformable models. The goal of this work is to extend active shape models (ASM) of tree-dimensional objects to the problem of 4D (3D + time) cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that encodes the spatio-temporal variability of a training set, i.e., the principal modes of variation of the temporal shapes are computed using some statistical parameters. An active search is used in the segmentation process where an initial approximation of the spatio-temporal shape is given and the gray level information in the neighborhood of the landmarks is analyzed. The starting shape is able to deform so as to better fit the data, but in the range allowed by the point distribution model. Several time series consisting of eleven 3D images of cardiac CT are employed for the method validation. Results are compared with manual segmentation made by an expert. The proposed application can be used for clinical evaluation of the left ventricle mechanical function. Likewise, the results can be taken as the first step of processing for optic flow estimation algorithms.