Hyperpolarized 129Xe MRI is emerging as a powerful means to provide 3D quantitative mapping of ventilation, interstitial barrier uptake, and red blood cell transfer. However, this capability requires non-standard radial reconstruction and accurate lung segmentation to enable quantitative analysis. Such reconstruction and image processing would ideally be standardized and centralized to facilitate using 129Xe gas exchange MRI in multi-center clinical trials. To this end, we developed a neural-network based lung segmentation approach that automatically generates accurate masks. With this capability, we demonstrate a fully centralized processing pipeline for real-time reconstruction and quantitative reporting of 129Xe gas exchange MRI.