Deployable Ultrasound Segmentation Via Deformable Attention-Enhanced UNet

In medical ultrasound, segmentation of the pubic symphysis and fetal head is critical for automating fetal and maternal assessments, yet it remains challenging due to anatomical variability, low contrast, and imaging artifacts. In this work, we present DAUNet, an efficient and lightweight segmentation model designed for compute efficient clinical use in ultrasound workflows. The architecture builds upon the UNet backbone and introduces two key innovations: Deformable V2 Convolutions for capturing non-rigid anatomical boundaries, and SimAM-an attention mechanism that enhances spatial saliency without adding parameters. We validate DAUNet on the FHPS dataset, which involves the simultaneous detection of fetal head and pubic symphysis in transperineal ultrasound images. The model achieves competitive segmentation accuracy compared to state-of-the-art methods while maintaining a substantially smaller parameter footprint. Robustness experiments further demonstrate that DAUNet retains high performance under partial visibility, highlighting its potential for deployment in edge-based ultrasound systems. The proposed approach supports the broader goal of enabling efficient, and accessible AI for maternal-fetal healthcare.