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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.02288 |
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| _version_ | 1866917554499354624 |
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| author | Friedrich, Paul Bieder, Florentin Thieringer, Florian M. Cattin, Philippe C. |
| author_facet | Friedrich, Paul Bieder, Florentin Thieringer, Florian M. Cattin, Philippe C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02288 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning Friedrich, Paul Bieder, Florentin Thieringer, Florian M. Cattin, Philippe C. Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2603.02288 |