This dissertation presents a fully automatic segmentation algorithm for cardiac MR data. Some of the currently published methods are automatic, but they only work well in 2D and sometimes in 3D and do not perform well near the extremities (apex and base) of the heart. Additionally, they require substantial user input to make them feasible for use in a clinical environment. This dissertation introduces novel approaches to improve the accuracy, robustness, and consistency of existing methods. Segmentation accuracy can be improved by knowing as much about the data as possible. Accordingly, we compute a single 4D active surface that performs segmentation in space and time simultaneously. The segmentation routine can now take advantage of information from neighboring pixels that can be adjacent either spatially or temporally. Robustness is improved further by using confidence labels on shape priors. Shape priors are deduced from manual segmentation of training data. This data may contain imperfections that may impede proper manual segmentation. Confidence labels indicate the level of fidelity of the manual segmentation to the actual data. The contribution of regions with low confidence levels can be attenuated or excluded from the final result. The specific advantages of using the 4D segmentation along with shape priors and regions of confidence are highlighted throughout the thesis dissertation. Performance of the new method is measured by comparing the results to traditional 3D segmentation and to manual segmentation performed by a trained clinician.