Semi-supervised Echocardiography Video Segmentation via Anchor Semantic Awareness and Continuous Pseudo-label Reforging

Automatic and accurate echocardiography video segmentation is essential for efficient and repeatable measurements of key clinical functional indicators for diagnosis of cardiovascular diseases. However, it is an extremely challenging task to obtain high-quality segmentation results throughout the cardiac cycle owing to (1) the inherent speckle noise in echocardiography videos, (2) the complex dynamic motions of cardiac structures, and (3) the scarcity of annotated data. To comprehensively address these challenges, we propose a novel semi-supervised model, which can achieve accurate and real-time echocardiography video segmentation with very limited annotations. The proposed model has two core innovative technologies. First, we propose a new anchor semantic awareness (ASA) module composed of an anchor recalibration (ARC) scheme and a temporal semantic fusion (TSF) algorithm. The former refines ambiguous feature regions by aligning them with learnable anchors, and the latter propagates structural semantic prototypes across frames to enhance boundary delineation and temporal consistency. Second, based on ASA, we developed a continuous pseudo-label reforging (CPR) module to gradually integrates high-quality pseudo-label through lightweight channel-wise attention, and reforge pseudo labels to provide more robust supervision.We extensively evaluated our method on two benchmarking datasets: CAMUS and EchoNet-Dynamic; experimental results show that our model outperforms SOTAs in segmentation accuracy while maintaining real-time performance. Codes will be publicly available upon publication.