Ye Zi, Chen Tianxiang, Wang Fangyijie, Zhang Hanwei, Zhang Lijun
Institute of Intelligent Software, Guangzhou, 511400, Guangdong, China.
University of Science and Technology of China, Hefei, 230026, Anhui, China.
Sci Rep. 2025 Sep 1;15(1):32152. doi: 10.1038/s41598-025-16797-6.
Segmenting echocardiographic images is a crucial step in assessing heart function, as clinical indicators can be obtained by precisely delineating the left ventricle. The success of subsequent heart analyses depends entirely on the precision of this segmentation. However, echocardiography is characterized by ambiguity and heavy background noise interference, making accurate segmentation more challenging. 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 (Discrete Wavelet Transform) Perona-Malik Diffusion (PMD) Block, we introduce a block that suppresses noise 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. Specifically, on the Pediatric PSAX (8959 images) and Pediatric A4C datasets (6425 images), we achieved Dice scores of 0.922 and 0.906, respectively; on the EchoNet-Dynamic dataset (19882 images), we achieved a Dice score of 0.931. 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.
对超声心动图图像进行分割是评估心脏功能的关键步骤，因为通过精确描绘左心室可以获得临床指标。后续心脏分析的成功完全取决于这种分割的精度。然而，超声心动图具有模糊性和严重的背景噪声干扰，使得准确分割更具挑战性。目前的方法缺乏效率，并且由于噪声干扰，容易误分割一些背景噪声区域，如左心室区域。为了解决这些问题，我们引入了P-Mamba，它集成了专家混合（MoE）概念，用于高效的儿科超声心动图左心室分割。具体来说，我们利用视觉曼巴最近提出的ViM层来提高我们模型的计算和内存效率，同时对全局依赖性进行建模。在基于离散小波变换（DWT）的佩罗纳-马利克扩散（PMD）模块中，我们引入了一个在保留左心室局部形状线索的同时抑制噪声的模块。因此，我们提出的P-Mamba创新性地将PMD的噪声抑制和局部特征提取能力与曼巴的高效全局依赖性建模设计相结合。我们在两个儿科超声数据集和一个通用超声数据集（即Echonet-dynamic）上进行了分割实验，并取得了领先的（SOTA）结果。具体来说，在儿科PSAX（8959幅图像）和儿科A4C数据集（6425幅图像）上，我们分别获得了0.922和0.906的Dice分数；在EchoNet-Dynamic数据集（19882幅图像）上，我们获得了0.931的Dice分数。利用P-Mamba模块的优势，我们的模型与已有的模型相比，包括具有二次和线性计算复杂度的视觉Transformer，展示了更高的准确性和效率。
