Localized Latent Editing for Dose-Response Modeling in Botulinum Toxin Injection Planning
Est\`ephe Arnaud, Mohamed Daoudi, and Pierre Guerreschi

TL;DR
This paper introduces a localized latent editing framework using StyleGAN2 to simulate and optimize Botulinum Toxin injections for facial asymmetry, enabling precise, region-specific dose planning with clinician interaction.
Contribution
It presents a novel Region-Specific Latent Axis Discovery method for localized facial control and a dose-response model linking latent trajectories to toxin units, improving injection planning accuracy.
Findings
Moderate-to-strong correlation between latent trajectories and asymmetry metrics
Generative model captures morphological change directions effectively
Hybrid clinician-in-the-loop workflow enhances practical application
Abstract
Botulinum toxin (Botox) injections are the gold standard for managing facial asymmetry and aesthetic rejuvenation, yet determining the optimal dosage remains largely intuitive, often leading to suboptimal outcomes. We propose a localized latent editing framework that simulates Botulinum Toxin injection effects for injection planning through dose-response modeling. Our key contribution is a Region-Specific Latent Axis Discovery method that learns localized muscle relaxation trajectories in StyleGAN2's latent space, enabling precise control over specific facial regions without global side effects. By correlating these localized latent trajectories with injected toxin units, we learn a predictive dose-response model. We rigorously compare two approaches: direct metric regression versus image-based generative simulation on a clinical dataset of N=360 images from 46 patients. On a hold-out…
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Taxonomy
TopicsBotulinum Toxin and Related Neurological Disorders · Facial Rejuvenation and Surgery Techniques · Facial Nerve Paralysis Treatment and Research
