Accurate segmentation of the right ventricle is a necessary precursor for the assessment of cardiac function. However, the large shape variations exhibited by the right ventricle make automated segmentation a difficult problem. In this work, we explore the ability of a cylindrical shape model to compactly represent and accurately segment this wide range of morphologies. The novelty of this method lies in the design of the fitting function which incorporates learned shape information into a Markov Random Field formulation. Furthermore, the shape model is integrated with a 2D image-based segmentation method, further refining the accuracy of the extracted regions. To evaluate our method, we applied it to the independently evaluated MICCAI RV Segmentation Challenge dataset. Our method performed as well as, or better than, the state-of-the-art methods, validating its suitability for this difficult application.