用生成模型模拟肉毒素注射效果，实现精准局部塑形。
Localized Latent Editing for Dose-Response Modeling in Botulinum Toxin Injection Planning
- 在风格化生成器潜空间中发现区域特异性肌肉松弛轨迹
- 通过360张临床图像建立剂量-反应模型，预测面部形态变化
- 结合医生交互式调整，兼顾医学与美学需求
肉毒素注射是矫正面部不对称和美容年轻化的标准疗法，但最佳剂量仍依赖经验判断，常导致效果不佳。本文提出一种局部潜空间编辑框架，通过剂量-反应建模模拟注射效果以辅助注射规划。核心贡献是区域特异性潜空间轴发现方法，在StyleGAN2潜空间中学习特定面部区域的肌肉松弛轨迹，实现局部精确控制且无全局副作用。通过将这些轨迹与注射单位关联，构建预测性剂量-反应模型。在包含46名患者共360张图像的临床数据集上，对比了直接度量回归与基于图像的生成模拟两种方法。在独立测试集上，几何不对称度量显示出中等到强的结构相关性，证明生成模型正确捕捉了形态变化方向。尽管生物个体差异限制了绝对精度，我们提出‘人机协同’工作流，让临床医生交互式优化模拟结果，弥合病理重建与美容规划之间的差距。
原文摘要 · Abstract (English)
Botulinum toxin (Botox) injections are the gold standard for managing facial asymmetry and aesthetic rejuvenation, yet determining the optimal dosage remains largely intuitive, often leading to suboptimal outcomes. We propose a localized latent editing framework that simulates Botulinum Toxin injection effects for injection planning through dose-response modeling. Our key contribution is a Region-Specific Latent Axis Discovery method that learns localized muscle relaxation trajectories in StyleGAN2's latent space, enabling precise control over specific facial regions without global side effects. By correlating these localized latent trajectories with injected toxin units, we learn a predictive dose-response model. We rigorously compare two approaches: direct metric regression versus image-based generative simulation on a clinical dataset of N=360 images from 46 patients. On a hold-out test set, our framework demonstrates moderate-to-strong structural correlations for geometric asymmetry metrics, confirming that the generative model correctly captures the direction of morphological changes. While biological variability limits absolute precision, we introduce a hybrid "Human-in-the-Loop" workflow where clinicians interactively refine simulations, bridging the gap between pathological reconstruction and cosmetic planning.
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