In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is important since it can determine whether urgent intervention is required in many emergencies. However, echocardiography is characterized by ambiguity and heavy background noise interference, bringing more difficulty to accurate segmentation. Present methods lack efficiency and are also prone to mistakenly segmenting some background noise areas as the left ventricular area due to noise disturbance. To relieve the two issues, we introduce P-Mamba for efficient pediatric echocardiographic left ventricular segmentation. Specifically, we turn to the recently proposed vision mamba layers in our vision mamba encoder branch to improve the computing and memory efficiency of our model 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 simultaneously preserving the local shape cues of the left ventricle. Leveraging the strengths of both the two encoder branches, P-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.

翻译：在儿科心脏病学中，通过超声心动图准确及时地评估心脏功能至关重要，因为这能决定许多急症中是否需要紧急干预。然而，超声心动图存在模糊性且受严重背景噪声干扰，给精准分割带来更大困难。现有方法效率不足，且易因噪声干扰将部分背景噪声区域误分割为左心室区域。为解决这两个问题，我们提出P-Mamba以实现高效的儿科超声心动图左心室分割。具体而言，我们在视觉Mamba编码器分支中引入近期提出的视觉Mamba层，以在建模全局依赖关系的同时提升模型的计算与内存效率。在另一基于DWT的PMD编码器分支中，我们设计了基于DWT的Perona-Malik扩散（PMD）模块，利用PMD抑制噪声，同时保留左心室的局部形态线索。通过融合这两个编码器分支的优势，P-Mamba相比已有模型（如二次与线性计算复杂度的视觉Transformer）实现了更优的准确性与效率。这一创新方法有望在儿科心脏成像及其他领域带来重大进展。