This study presents a deep learning-based approach for the automatic segmentation of the right ventricle (RV) in gated myocardial perfusion SPECT images (gSPECT). Unlike the left ventricle (LV), the RV poses significant segmentation challenges due to its complex anatomy, thinner walls, and lower perfusion. We manually annotated 384 SPECT volumes and propose the use of the ResUNetSE3D neural network, incorporating both anatomical and phase imaging data to enhance segmentation accuracy. The model achieved a Dice coefficient of 0.8272 and a Jaccard index of 0.7086. These results demonstrate the feasibility of fully automated RV segmentation, laying the groundwork for future clinical applications in quantitative cardiac assessment.