Myocarditis, an acute cardiac disorder progressing rapidly to life-threatening heart failure, requires precise lesion segmentation from Cine Magnetic Resonance Imaging (Cine-MRI) for timely intervention. Current segmentation accuracy is limited by two key challenges: 1) spatiotemporal discordance between myocardial motion patterns and evolving pathological features and 2) morphological complexity (irregular borders, scattered lesions). In this paper, we propose the MG-Mamba, a framework integrating deep state space models with graph-based spatiotemporal analysis. The architecture employs Mamba blocks to establish initial intra-/inter-frame dependencies in Cine-MRI sequences. For Challenge 1, we improve the detection of subtle abnormal motions through multi-step cross-frame analysis, extending beyond conventional adjacent-frame analysis. For Challenge 2, we further implement multi-scale patch division and constructs inter-patch graphs to concurrently capture global lesion distribution and local geometric patterns. Extensive evaluations on SYC-QC and SYC-SX clinical datasets demonstrate MG-Mamba’s superior segmentation accuracy over ten state-of-the-art benchmarks, significantly advancing myocarditis diagnostic precision. The code is available at https://github.com/userZ-CY/MICCAI.