  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. To address these issues, we
introduce P-Mamba, which integrates the Mixture of Experts (MoE) concept for
efficient pediatric echocardiographic left ventricular segmentation.
Specifically, we utilize the recently proposed ViM layers from the vision mamba
to enhance our model's computational and memory efficiency while modeling
global dependencies.In the DWT-based Perona-Malik Diffusion (PMD) Block, we
devise a PMD Block for noise suppression while preserving the left ventricle's
local shape cues. Consequently, our proposed P-Mamba innovatively combines the
PMD's noise suppression and local feature extraction capabilities with Mamba's
efficient design for global dependency modeling. We conducted segmentation
experiments on two pediatric ultrasound datasets and a general ultrasound
dataset, namely Echonet-dynamic, and achieved state-of-the-art (SOTA) results.
Leveraging the strengths of the P-Mamba block, our model demonstrates superior
accuracy and efficiency compared to established models, including vision
transformers with quadratic and linear computational complexity.
