指出医学图像分割评估中的拓扑错误陷阱，推动更公平的评测标准
Pitfalls of topology-aware image segmentation
- 发现现有评估中连接性选择不当、标注遗漏拓扑瑕疵等关键问题
- 实证显示这些问题严重影响模型排名与结果可靠性
- 提出可操作建议，助力构建公正可靠的拓扑感知分割评测体系
拓扑正确性（即形状结构完整性和特定特征的保留）是医学图像分割任务（如神经元或血管分割）的基本要求。尽管近年来涌现出大量拓扑感知方法应对该挑战，但其实际应用受限于存在缺陷的基准测试实践。本文揭示了模型评估中的关键陷阱，包括连接性选择不充分、地面真值标注中忽略拓扑伪影，以及评估指标使用不当。通过详细的实证分析，我们发现这些问题对分割方法的评估和排名产生了深远影响。基于研究发现，我们提出一系列可执行的改进建议，旨在建立公平且稳健的拓扑感知医学图像分割评估标准。
原文摘要 · Abstract (English)
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuron or vessel segmentation. Despite the recent surge in topology-aware methods addressing this challenge, their real-world applicability is hindered by flawed benchmarking practices. In this paper, we identify critical pitfalls in model evaluation that include inadequate connectivity choices, overlooked topological artifacts in ground truth annotations, and inappropriate use of evaluation metrics. Through detailed empirical analysis, we uncover these issues' profound impact on the evaluation and ranking of segmentation methods. Drawing from our findings, we propose a set of actionable recommendations to establish fair and robust evaluation standards for topology-aware medical image segmentation methods.
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