Marrying Perona Malik diffusion with Mamba for efficient pediatric echocardiographic left ventricular segmentation.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiology

Background:

• Accurate left ventricular segmentation in echocardiography is vital for assessing heart function.
• Echocardiographic images present challenges due to noise and ambiguity, hindering precise segmentation.
• Existing methods often lack efficiency and misinterpret background noise as cardiac structures.

Purpose of the Study:

• To introduce P-Mamba, an efficient model for segmenting pediatric echocardiographic left ventricles.
• To leverage Mixture of Experts (MoE) and Vision Mamba (ViM) layers for improved computational and memory efficiency.
• To develop a method that effectively suppresses noise while preserving crucial local shape information of the left ventricle.

Main Methods:

• P-Mamba integrates Mixture of Experts (MoE) with Vision Mamba (ViM) layers for efficient global dependency modeling.
• A Discrete Wavelet Transform (DWT)-based Perona-Malik Diffusion (PMD) Block is employed for noise suppression and local feature preservation.
• The model combines PMD's noise reduction and local cue extraction with Mamba's efficient global modeling capabilities.

Main Results:

• P-Mamba achieved state-of-the-art (SOTA) results on multiple echocardiographic datasets.
• Achieved Dice scores of 0.922 on the Pediatric PSAX dataset and 0.906 on the Pediatric A4C dataset.
• Attained a Dice score of 0.931 on the general EchoNet-Dynamic dataset, outperforming existing models.

Conclusions:

• P-Mamba demonstrates superior accuracy and efficiency in echocardiographic left ventricular segmentation.
• The model effectively addresses challenges posed by noise and ambiguity in ultrasound images.
• P-Mamba offers a significant advancement over current segmentation techniques, particularly for pediatric cardiac assessments.