Tree-Skeleton Collaborative Learning Promotes Topological Completeness in Tree-like Structure Segmentation

doi:10.1007/s44196-026-01357-1

2026-05-12

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DOI：10.1007/s44196-026-01357-1

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树状结构（如气道和血管树）的精确分割对于医学图像分析至关重要。然而，由于这些结构固有的拓扑复杂性，这项任务具有挑战性。此外，这种复杂性所带来的巨大数据标注成本往往导致实践中出现不完整的标注。为解决这些挑战，我们提出了一种名为Tree-Skeleton Collaborative Learning（TSCL）的新型框架。通过整合树和骨架分割并进行偏差校正，TSCL显著提升了拓扑完整性。本研究的核心思想是利用骨架引导和动态伪标签生成来增强细小分支的检测和连续性。此外，我们引入了Expectation-Maximization（EM）算法以处理标注偏差，识别并纠正不完整标签中缺失的分支。该方法使TSCL能够在提升分割拓扑完整性的同时，保持高性能的准确性。在两个公开数据集（BAS和PARSE）上的实验表明，TSCL在拓扑完整性指标上优于当前最先进的方法，从而证明了其在树状结构分割中的鲁棒性和可靠性。

Keyword:

Tree-like structure segmentation

Topological completeness

Incomplete annotations

Skeleton segmentation

期刊

International Journal of Computational Intelligence Systems

IF:

论文数:

362

被引数:

2.7K

机构

sichuan university

学者数:

12.1W

论文数: 7.8W

被引数: 100

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