Hybrid-Frequency Feature Evolution Network for Endoscopic Ultrasound Image Segmentation
Dongfang Wang, Tao Zhou, Jian Yu Yang · 2025
The segmentation of lesions in endoscopic ultrasound images is essential for clinical diagnosis. However, such as the low contrast in ultrasound images, patient-specific factors, and inter-case variations, make accurate segmentation highly challenging. To address these issues, we propose a novel Hybrid-frequency Feature Evolution Network (HFENet) for EUS image segmentation. Specifically, we propose a Hybrid-frequency Feature Evolution and Supervision (HFES) module that ensures consistent segmentation across multiple scales, mitigating the impact of patient-specific factors and inter-case variability. Additionally, by integrating wavelet transform, multi-scale features are decomposed into low- and high-frequency components to capture global structures and fine details, respectively. A Selective Co-Evolution (SCE) module is also proposed to adaptively balance these components to enhance feature representation. Experimental results on three EUS datasets show that our HFENet outperforms state-of-the-art segmentation methods.