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评估心脏功能至关重要，因为这在许多急症中可决定是否需要紧急干预。然而，超声心动图存在模糊性和强背景噪声干扰的问题，给精确分割带来了更大困难。现有方法效率不足，且容易因噪声干扰将部分背景噪声区域误分割为左心室区域。针对这两个问题，我们提出P-Mamba以实现儿科超声心动图左心室的高效分割。具体而言，我们在视觉mamba编码器分支中采用近期提出的视觉mamba层，在建模全局依赖关系的同时提升模型的计算和内存效率。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，该模块利用PMD进行噪声抑制，同时保留左心室的局部形状特征。通过结合两个编码器分支的优势，P-Mamba在准确性和效率上均优于现有模型（如具有二次和线性计算复杂度的视觉Transformer）。这一创新方法有望推动儿科心脏成像及其他领域的重大进展。