用几何约束优化传输方法，精准生成冠状动脉造影狭窄图像。
Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport
- 将局部编辑建模为带几何约束的熵正则最优传输问题
- 在ARCADE数据集上检测精度提升27.8%，多中心数据集提升23.0%
- 适合需要高精度狭窄图像生成的医学影像算法研发者
冠状动脉造影（CAG）狭窄的高质量影像数据稀缺，限制了自动化检测的临床应用。合成狭窄数据可有效扩充训练集，提升数据质量、多样性与分布覆盖，增强检测精度与泛化能力。然而，基于扩散模型的编辑通常依赖噪声初始化反向过程中的软引导，难以实现像素级精度与结构保持。本文提出OT-Bridge编辑器，将局部编辑重构为带几何约束的熵正则最优传输（entropic optimal transport, OT）问题，利用几何信息引导生成路径，实现更强的几何控制。大量实验表明，所生成的血管造影图像显著提升下游狭窄检测性能，在公开的ARCADE基准上相对增益达27.8%，在自建多中心数据集上达23.0%，且定性结果一致优异。
原文摘要 · Abstract (English)
The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the clinical translation of automated stenosis detection. Synthetic stenosis data provides a practical avenue to augment training sets, improving data quality, diversity, and distributional coverage, and enhancing detection precision and generalization. However, diffusion-based editing commonly relies on soft guidance in a noise-initialized reverse process, offering limited pixel-level precision and structure preservation. We propose the OT-Bridge Editor, which reframes localized editing as a constrained entropic optimal transport (OT) problem and leverages geometric information to steer the generation path, enabling stronger geometric control. Extensive experiments show that our synthesized angiograms consistently improve downstream stenosis detection, yielding substantial relative gains of 27.8% on the public ARCADE benchmark and 23.0% on our multi-center dataset, supported by consistent qualitative results.
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