Wavelet attention fusion for semi-supervised ultrasound segmentation

Ultrasound image segmentation plays a vital role in medical diagnosis. However, automatic segmentation remains a significant challenge due to the presence of noise, low contrast, and the limited availability of annotated data. This paper proposes a novel semi-supervised segmentation approach, termed WAF (Wavelet Attention Fusion) . The method applies discrete wavelet transform (DWT) to decompose ultrasound images into sub-bands of different frequencies, primarily utilizing the low-frequency components for global feature representation, while the high-frequency components capture fine details and edges. To improve the model’s ability to focus on critical regions, we introduce an attention fusion module that integrates both channel and spatial attention mechanisms. This design effectively enhances the model’s perception of important frequency and spatial features on low resolution ultrasound images. Experiments on multiple ultrasound segmentation datasets demonstrate that WAF consistently outperforms traditional FixMatch and other state-of-the-art semi-supervised methods. Specifically, WAF yields Dice score improvements of +1.38%, +2.41%, and +3.88% over FixMatch on HC18, DDTI, and CCAUI, respectively. Ablation studies further confirm the essential role of wavelet decomposition and dual attention in boosting performance. Our findings suggest that WAF can significantly improve semi-supervised medical image segmentation while reducing reliance on labeled data. The code is publicly available at https://github.com/wxmadm/WAF .