EFAU-Net: efficient feature-enhanced U-Net for early pregnancy ultrasound segmentation

Abstract

Early-pregnancy ultrasound images often contain gestational sac, yolk sac, and embryo regions with blurred boundaries, variable shapes, and small target sizes, which makes accurate segmentation difficult. In addition, many existing segmentation networks have high computational cost and are not well suited for resource-constrained clinical devices. To address these challenges, we propose EFAU-Net, an efficient feature-enhanced U-shaped network for segmenting key structures in early-pregnancy ultrasound images. EFAU-Net introduces three components: a grouped channel shuffle fusion (GCSF) module to improve feature utilization in deep layers with low computational overhead, a local attention enhancement (LAE) module to strengthen multi-scale feature fusion and small-target representation, and a ground-truth boundary generation (GBG) module to enhance boundary supervision and improve contour alignment. Experiments show that EFAU-Net achieves Dice scores of 97.14%, 91.25%, and 81.83% for gestational sac, yolk sac, and embryo segmentation, respectively, with 0.826 M parameters for each target-specific binary segmentation model. Compared with representative baseline and competing methods, the proposed model provides a better balance between segmentation accuracy and model complexity. These results indicate that EFAU-Net is effective for segmenting key anatomical structures in early-pregnancy ultrasound images. In the supplementary video-level biometric measurement analysis, EFAU-Net was further applied frame by frame to retained ultrasound videos, and the maximum measured value across frames was used as the video-level result, providing a useful basis for subsequent quantitative image analysis.