Region Features Propagation with Class-Aware Contrastive Learning for Weakly Supervised Cardiac Segmentation

Cardiac segmentation is crucial for analyzing heart structure and func tion, providing essential support for clinical diagnosis and treatment planning. However, obtaining fully annotated images is both costly and time-consuming. Scribble annotations, which utilize simple lines instead of pixel-wise annotations, offer a cost-effective alternative but lack sufficient information, making segmentation network training challenging. To address this, we propose ScribbleCorrNet SCN , a novel framework for scribble-supervised medical image segmentation. SCN employs Correlation-Aware Label Enhancement CALE strategies, introducing two key mechanisms: i pixel affinity propagation PAP , which propagates high-confidence pixels using pairwise similarities in a correlation map, and ii region shape refinement RSR , which refines pseudo-labels by leveraging shape information encoded in the correlation map. Additionally, a class-aware contrastive learning CACL mechanism enhances intra-class consistency and inter-class separation. Experiments on the ACDC2017 and MSCMR datasets demonstrate SCN’s superior performance compared to existing scribble-based segmentation methods.