In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is crucial since it can determine whether urgent intervention is required in many emergencies. However, echocardiography is characterized by ambiguity and heavy background noise interference, causing more difficulty in accurate segmentation. Present methods lack efficiency and are prone to mistakenly segmenting some background noise areas, such as the left ventricular area, due to noise disturbance. We introduce LV-Mamba for efficient pediatric echocardiographic left ventricular segmentation to relieve the two issues. Specifically, we turn to the recently proposed vision mamba layers in our vision mamba encoder branch to improve our model’s computing and memory efficiency while modeling global dependencies. In the other DWT-based PMD encoder branch, we devise DWT-based Perona-Malik Diffusion (PMD) Blocks that utilize PMD for noise suppression while preserving the left ventricle’s local shape cues. Leveraging the strengths of both encoder branches, LV-Mamba achieves superior accuracy and efficiency to established models, such as vision transformers with quadratic and linear computational complexity. This innovative approach promises significant advancements in pediatric cardiac imaging and beyond.