Automated ventricular function analysis can enhance healthcare consistency and accessibility, particularly in resource-limited settings. Current segmentation methods trained on adult heart ultrasounds struggle to accurately outline the irregular shape of the left ventricle owing to their limited exploration of border features. HFE-Mamba is introduced for left ventricle segmentation with shape awareness in order to address the existing challenge. Therefore, this proposal introduces the High-Frequency Enhancement Block (HFEB), enhancing the high-frequency component of left ventricles, particularly the boundary area in pediatric echocardiograms. Moreover, this also facilitates the investigation of target boundary specifics while extracting features. The incorporation of newly suggested vision mamba layers into encoder and decoder branches enhances the model’s computational and memory efficiency while capturing global dependencies. Tests conducted on two publicly available datasets indicate the superior predictive accuracy of the HFE-Mamba model in identifying target shapes.