Automatic Segmentation, Inpainting, and Tracking of CMEs By A Pixel-Annotation-Free System

Coronal mass ejections (CMEs), one of the most significant and intense solar eruptive activities, exert profound impacts on Earth and the interplanetary space environment. Consequently, prompt detection and tracking of CMEs are important for mitigating their impacts. Considering the complexity of manually annotating regions of CME on coronagraph images and the presence of anomalous data, we have developed a new automatic CME tracking system that does not rely on pixel-level annotations and can handle obvious data errors. The proposed system consists of three processes: error area segmentation and inpainting, CME segmentation, and CME tracking. All deep learning algorisms in our system are trained on the dataset without pixel-level labels, which can be easily constructed from publicly available CME catalogs. Moreover, by comparison with existing catalogs and methods, we demonstrate that the proposed system is reliable in providing CME initial kinematics, facilitating future studies on the origin and propagation of CMEs.