Wavelet-Based Frequency Replacement and Edge Enhancement for Semi-Supervised Fetal Ultrasound Image Segmentation.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Published Date: Jan 8, 2026

Abstract

OBJECTIVES: Ultrasound image segmentation remains a significant challenge due to inherent low contrast and blurred anatomical boundaries. Fully supervised deep learning approaches require extensive annotated datasets, which are costly and labor-intensive to acquire. This study aims to develop an effective semi-supervised segmentation framework for ultrasound images with limited annotations. METHODS: We propose a novel semi-supervised segmentation framework tailored for ultrasound images, leveraging frequency component augmentation and edge mask enhancement to promote structural consistency between weakly and strongly augmented inputs. Specifically, discrete wavelet transform (DWT) is used to decompose ultrasound images into low-frequency and high-frequency sub-bands. A high-frequency component replacement strategy is introduced for strongly augmented images, and an edge mask enhancement module is designed to further emphasize anatomical boundaries. RESULTS: Experiments conducted on 3 public fetal ultrasound imaging segmentation datasets-PSFHS, HC18, and CCAUI-demonstrate that our method achieves average Dice similarity coefficients (DSC) of 0.81 and 0.91, respectively, using only 10 annotated images. This represents a 2-3% DSC improvement over existing semi-supervised methods such as FixMatch. Ablation studies confirm the effectiveness of both the high-frequency augmentation and edge enhancement components. CONCLUSION: The proposed framework offers a promising direction for ultrasound image segmentation in settings with limited annotations, effectively improving segmentation accuracy by combining frequency-domain augmentation and edge-aware enhancement. Code will be available at https://github.com/apple1986/WTEM-SemiSeg.

Authors

  • Wenbo Yue
    School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China.
  • Xiaming Wu
    School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China.
  • Qing Huang
    Department of Environmental Health and Occupational Medicine,West China School of Public Health,Sichuan University,Chengdu 610041,China.
  • Xinglong Wu
    School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Chang Li
    Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
  • Yajun Yu
    Department of Medical Physics, Wuhan University, Wuhan, 430072, China.
  • Jun Lyu
    Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
  • Guoping Xu
    School of Computer Sciences and Engineering, Wuhan Institute of Technology, Wuhan, Hubei, China.

Keywords

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