BECM-Net: A Multi-granularity Collaborative Framework for Semi-Supervised Fetal Ultrasound Segmentation.

Abstract

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.