AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning

TL;DR

AutoFFS introduces a data-driven, adversarial deformation framework that generates quantitative skull morphologies for improved facial feminization surgery planning, addressing the lack of objective tools in current clinical practice.

Contribution

It presents a novel method for creating counterfactual skull shapes using adversarial free-form deformations to aid surgical planning in FFS.

Findings

Generated morphologies successfully exhibit target sex characteristics.

Human perceptual study confirms realism of the deformed skulls.

Classifier evaluation shows effective transformation toward target sex.

Abstract

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.