HFE-RWKV: High-Frequency Enhanced RWKV Model for Efficient Left Ventricle Segmentation in Pediatric Echocardiograms

Automated ventricular function analysis can improve healthcare in resource-scarce areas, but current segmentation methods struggle with accurately delineating the irregular shape of the left ventricle due to a lack of emphasis on exploring the high-frequency target boundary features, and computational inefficiency is another concern. To address the two challenges, we turn to a novel and efficient basic structure, RWKV, and propose High-Frequency Enhanced RWKV (HFE-RWKV) for accurate and efficient left ventricle segmentation. Specifically, we propose the HFE-RWKV block as the encoder's core to augment the high-frequency component, which is also the boundary area of the left ventricles in pediatric echocardiograms. In this way, the target boundaries can be explored more adequately during feature extraction. We propose space-frequency consistency loss to refine the shape of predicted masks further. Specifically, our new loss function incorporates spatial and frequency domain loss components to jointly refine predicted mask shapes in cases where current spatial-domain segmentation losses cannot be optimized further. Experiments on two public datasets prove our HFE-RWKV's superiority in accuracy and efficiency. Specifically, our HFE-RWKV outperforms U-Mamba [12] by 2% in Dice Similarity Coefficient (DSC) while using only 67% of the parameters and 26% of the computational complexity. The code is available at https://github.com/yezizi1022/HFE-RWKV.