Myasthenia Gravis (MG) is an autoimmune neuromuscular disease. Given that extraocular muscle manifestations are the initial and primary symptoms in most patients, ocular muscle assessment is regarded necessary early screening tool. To overcome the limitations of the manual clinical method, an intuitive idea is to collect data via imaging devices, followed by analysis or processing using Deep Learning (DL) techniques (particularly image segmentation approaches) to enable automatic MG evaluation. Unfortunately, their clinical applications in this field have not been thoroughly explored. To bridge this gap, our study prospectively establishes a new DL-based system to promote the diagnosis of MG disease, with a complete workflow including facial data acquisition, eye region localization and ocular structure segmentation. Experimental results demonstrate that the proposed system achieves superior segmentation performance of ocular structure. Moreover, it markedly improves the diagnostic accuracy of doctors. In the future, this endeavor can offer highly promising MG monitoring tools for healthcare professionals, patients, and regions with limited medical resources.