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AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning

Paul Friedrich, Florentin Bieder, Florian M. Thieringer, Philippe C. Cattin

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.

AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning

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.
Paper Structure (16 sections, 7 equations, 3 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 7 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Problem Formulation
  5. Classifier Training
  6. Free-Form Deformation
  7. Regularizing the Deformation Field
  8. Deformation-Based Targeted Adversarial Attack
  9. Experiments
  10. Dataset
  11. Classifier Training & Performance
  12. System Level Performance
  13. Human evaluation
  14. Conclusion
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Overview of the proposed pipeline. We perform a deformation-based targeted adversarial attack on an ensemble of pre-trained binary sex classifiers by optimizing the deformation field given the target class label $y_{\text{target}}$. Illustration is in 2D for visualization purposes, but the pipeline works in 3D.
  • Figure 2: Class probabilities from the hold-out classifier ensemble after applying our proposed framework with (top), and without (bottom) the ensembling strategy.
  • Figure 3: Example images of a transformed skull. (Left) Example FFS from a lateral view. (Middle) Example FMS from a frontal view. (Right) Example without the B-spline formulation and regularization terms. Best viewed zoomed.