Tissue tracking post processing from cardiac cine MRI can be used to calculate myocardial deformation parameters without additional scans. One major drawback of the processing is reduced reliability due to interference from blood and trabecular muscle signals and varying image contrast. Manual segmentation of LV myocardium can improve the robustness but is time consuming. We developed a deep convolutional neural network to automatically segment myocardium and used symmetric deformable registration to obtain the tracking information from the resulting binary masks. The segmentation and tracking worked reliably well, resulting in accurate pixel movement trajectories.