AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning
This work addresses the need for quantitative and reproducible anatomical guidance in facial feminization surgery for transgender and gender diverse patients, representing a novel application in this domain.
The authors tackled the problem of subjective planning in facial feminization surgery by developing AutoFFS, a data-driven framework that generates counterfactual skull morphologies through adversarial deformations, validated via classifier-based evaluation and a human perceptual study to confirm target sex characteristics.
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 and a human perceptual study, confirming that the generated morphologies exhibit target sex characteristics.