提出裂缝分割拓扑评估的完整实现，提升结构准确性衡量能力
An Implementation of the Crack Topology Score with Extensions
- 基于骨架匹配框架评估裂缝连通性，超越传统像素指标
- 支持处理深度学习输出中的小孔与边缘噪声等常见伪影
- 提供可视化工具与可复现代码，适合模型评估与对比研究
裂纹拓扑评分（CTS）是一种近期提出的度量方法，专注于评估裂纹分割结果的拓扑正确性。与仅关注像素重叠的指标（如IoU或F1-score）不同，CTS采用基于骨架的匹配框架，量化连通性的保持程度。本文提供了对CTS的忠实实现，并引入可选的预处理扩展，用于处理深度学习输出中常见的预测伪影（如小孔和边缘噪声）。所有扩展默认关闭，以确保与原始定义严格可比。该实现支持PyTorch工作流，并包含可视化工具以增强透明性。代码与归档资源将发布于https://github.com/SH-Joo/crack-topology-score。
原文摘要 · Abstract (English)
The Crack Topology Score (CTS) is a recently proposed metric that focuses on evaluating the topological correctness of crack segmentation outputs. While pixel-wise metrics such as IoU or F1-score fail to capture structural validity, CTS offers a skeleton-based matching framework to measure the preservation of connectivity. This paper presents a faithful implementation of the CTS metric, along with optional preprocessing extensions designed to handle common prediction artifacts (e.g., small holes and edge noise) found in deep learning outputs. All extensions are disabled by default to ensure strict comparability with the original definition. The implementation supports PyTorch-based workflows and includes visualization tools for transparency. Code and archival resources will be made available at https://github.com/SH-Joo/crack-topology-score.
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