A sequence-based deep learning pipeline is proposed for automated processing of gated SPECT studies of the right ventricle. The model processes a temporal sequence of 3D tomographic volumes and predicts right-ventricular myocardial masks for each cardiac phase. Based on the predicted segmentation masks, clinical parameters are computed and perfusion polar maps of the “bull's eye” type, as well as magnitude and phase planar maps, are constructed. In experiments, the recurrent model achieved a Dice score of up to 0.8119.