通过一致性匹配提升病理图像分割的拓扑准确性
MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation
- 利用多重扰动预测强制拓扑一致性
- 在无标注情况下显著降低拓扑错误率
- 适合需要高精度分割的医学影像分析场景
在半监督分割中，从无标签数据中捕捉有意义的语义结构至关重要，尤其在病理图像中物体密集分布时更具挑战性。为此，我们提出一种框架，旨在稳健地识别并保留关键拓扑特征。方法通过随机丢弃和时间训练快照生成多个扰动预测，并强制这些输出间的拓扑一致性，从而区分生物上合理的结构与瞬态噪声。核心挑战在于缺乏真实标签时如何准确匹配不同预测间的对应拓扑特征。为此，我们引入一种新匹配策略，结合空间重叠与全局结构对齐，最小化预测间差异。大量实验表明，该方法有效减少拓扑误差，实现更鲁棒、精确的分割，对下游可靠分析至关重要。代码已开源。
原文摘要 · Abstract (English)
In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where objects are densely distributed. To address this issue, we propose a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features. Our method leverages multiple perturbed predictions obtained through stochastic dropouts and temporal training snapshots, enforcing topological consistency across these varied outputs. This consistency mechanism helps distinguish biologically meaningful structures from transient and noisy artifacts. A key challenge in this process is to accurately match the corresponding topological features across the predictions in the absence of ground truth. To overcome this, we introduce a novel matching strategy that integrates spatial overlap with global structural alignment, minimizing discrepancies among predictions. Extensive experiments demonstrate that our approach effectively reduces topological errors, resulting in more robust and accurate segmentations essential for reliable downstream analysis. Code is available at https://github.com/Melon-Xu/MATCH.
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