让医学影像生成可精准修改局部结构，避免全局错误。
Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis
- 按解剖区域细分结构，实现局部控制生成
- 在冠脉CT上生成真实且区域特定的病变变化
- 适合疾病进展建模与精准数据增强
反事实图像生成可用于可控数据增强、偏见缓解和疾病建模。现有方法依赖外部分类器或回归器，仅能处理个体层面因素（如年龄），难以生成局部结构变化，常导致全局伪影。像素级引导虽使用分割掩码，但需用户手动定义反事实掩码，操作繁琐。已有段落引导的反事实微调（Seg-CFT）通过分割导出的测量值监督结构特异性变量，但仍限于全局干预。本文提出位置分割引导反事实微调（Pos-Seg-CFT），将每个结构细分为区域段，每区域独立提取测量值，实现空间局部化且解剖一致的反事实生成。在冠状动脉CT血管造影数据集上的实验表明，Pos-Seg-CFT可生成逼真、区域特定的修改，为疾病进展建模提供更精细的空间控制。
原文摘要 · Abstract (English)
Counterfactual image generation enables controlled data augmentation, bias mitigation, and disease modeling. However, existing methods guided by external classifiers or regressors are limited to subject-level factors (e.g., age) and fail to produce localized structural changes, often resulting in global artifacts. Pixel-level guidance using segmentation masks has been explored, but requires user-defined counterfactual masks, which are tedious and impractical. Segmentor-guided Counterfactual Fine-Tuning (Seg-CFT) addressed this by using segmentation-derived measurements to supervise structure-specific variables, yet it remains restricted to global interventions. We propose Positional Seg-CFT, which subdivides each structure into regional segments and derives independent measurements per region, enabling spatially localized and anatomically coherent counterfactuals. Experiments on coronary CT angiography show that Pos-Seg-CFT generates realistic, region-specific modifications, providing finer spatial control for modeling disease progression.
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