Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labels and produces fragmented or topologically invalid masks. We propose Topology Graph Consistency (TGC), a framework that integrates graph-theoretic constraints by aligning Laplacian spectra, component counts, and adjacency statistics between prediction graphs and references. This enforces global topology and improves segmentation accuracy. Experiments on GlaS and CRAG demonstrate that TGC achieves state-of-the-art performance under 5-10% supervision and significantly narrows the gap to full supervision.

翻译：半监督语义分割（SSSS）在计算病理学中至关重要，因为密集标注成本高昂且数量有限。现有方法通常依赖像素级一致性，这会传播噪声伪标签并产生碎片化或拓扑无效的掩码。我们提出了拓扑图一致性（TGC），这是一个通过对齐预测图与参考图之间的拉普拉斯谱、连通分量数量和邻接统计量来整合图论约束的框架。该方法强化了全局拓扑结构并提升了分割精度。在GlaS和CRAG数据集上的实验表明，TGC在5-10%的监督比例下实现了最先进的性能，并显著缩小了与全监督方法的差距。