Myocardial Perfusion Imaging (MPI) is the golden standard in detecting and assessing Coronary Artery Disease (CAD). Due to its role and incidence in diagnostics, automated and semi-automated solutions are available aiding diagnoses based on the perfusion and function of the left ventricle. Evaluation with Single-Photon Emission Computed Tomography (SPECT) cameras often takes >30 minutes and due to patient cardinality, shortened acquisition produces images with lower Signal-to-Noise Ratio (SNR), challenging automated solutions. Here a fully automated method is presented, solving the left ventricle (LV) segmentation problem. The method is a 3D U-Net-based, self-supervised learning (SSL) approach, with a rich augmentation pipeline. A brief study is presented where the proposed algorithm is compared to the current state-of-the-art solution in Machine Learning (ML), with the evaluation of various segmentation metrics of the left ventricular muscle tissue. The proposed method showed an effective > 89% precision on the segmentation task of MPI studies on both simulated and real datasets. In conclusion, the technique can aid the evaluation of MPI studies in clinical environments by automating the segmentation of studies with a high success rate.