Accurate segmentation of fetal ultrasound (US) images is essential for measuring the Angle of Progression (AoP) and assessing fetal head descent during labor. However, conventional semi-supervised learning (SSL) for ultrasound segmentation is challenged by inaccurate pseudo-labeling at blurred or low-contrast boundaries and by limited enforcement of consistency. To address these challenges, we propose the Boundary-Enhanced Collaborative Multi-granularity Network (BECM-Net), which, from a multi-granularity modeling perspective, can be interpreted as a unified framework that jointly optimizes pixel-level, region-level, and structure-level representations. Specifically, at the pixel level, a novel DirDiff-Conv module enhances boundary perception and texture representation through multi-orientation differential filtering, enabling fine-grained modeling of local structures. At the region level, the Uncertainty-Confidence Aligned Mix (UCA-Mix) strategy performs uncertainty-guided bidirectional region-level mixing, facilitating semantic alignment and reducing pseudo-label noise. At the structure level, the ContourRefine branch models object contours by integrating deep semantic features with shallow boundary cues while coupling boundary learning with pseudo-label supervision, thereby enforcing structural-level consistency in global shape and boundary continuity. Through collaborative optimization across multiple granularities, BECM-Net provides more reliable supervision and robust feature learning under limited annotations. Extensive experiments on fetal ultrasound datasets demonstrate that BECM-Net can achieve the state-of-the-art performance, with particularly notable gains in challenging regions with ambiguous pubic symphysis and fetal head boundaries.
Hu et al. (Thu,) studied this question.