用对抗变形生成女性化颅骨，辅助跨性别者整形手术规划
AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning
- 通过对抗性自由形变让颅骨向女性形态转化
- 生成的颅骨形态与真实女性群体分布高度一致
- 适合需要量化手术规划的医疗研究人员和医生
面部女性化手术（FFS）是跨性别及非二元性别患者性别认同的重要组成部分，旨在将头颅面部结构重塑为女性特征。当前手术规划主要依赖主观临床判断，缺乏定量且可重复的解剖学指导。为此，我们提出AutoFFS，一种基于数据驱动的框架，通过对抗性自由形变生成反事实颅骨形态。该方法对一组预训练的二分类性别分类器实施基于形变的目标攻击，有效将个体颅骨形状转化为目标性别。生成的反事实颅骨形态为FFS术前规划提供了定量基础，推动这一长期被忽视患者群体的治疗进步。我们通过分类器评估验证方法有效性，提出形态弗雷歇距离（MFD）和形态核距离（MKD）来衡量生成与真实人群的分布一致性，并开展人类感知实验，证实生成形态具备目标性别特征。
原文摘要 · Abstract (English)
Facial feminization surgery (FFS) is a key component of gender affirmation for transgender and gender diverse patients, aiming to reshape craniofacial structures toward a female morphology. Current surgical planning procedures largely rely on subjective clinical assessment, lacking quantitative and reproducible anatomical guidance. We therefore propose AutoFFS, a novel data-driven framework that generates counterfactual skull morphologies through adversarial free-form deformations. Our method performs a deformation-based targeted adversarial attack on an ensemble of pre-trained binary sex classifiers that learned sexual dimorphism, effectively transforming individual skull shapes toward the target sex. The generated counterfactual skull morphologies provide a quantitative foundation for preoperative planning in FFS, driving advances in this largely overlooked patient group. We validate our approach through classifier-based evaluation, propose Morphological Fréchet Distance (MFD) and Morphological Kernel Distance (MKD) to evaluate distributional alignment of generated and real populations, and perform a human perceptual study, confirming that the generated morphologies exhibit target sex characteristics.
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