Counterfactual 3D Pelvic Fracture CT Synthesis via Anatomy-Guided Geometric Priors and Region-Aware Latent Diffusion

Pelvic fractures are severe traumatic orthopedic injuries that exhibit an extreme long-tail data distribution, severely limiting the generalization of deep learning segmentation models on rare but clinically critical subtypes, such as Denis type-III sacral fractures. Unlike pathological anomalies that merely manifest as local texture changes, fractures involve complex geometric discontinuities of cortical bone and rigid displacements of detached fragments. Synthesizing such injuries requires anatomically valid fracture masks, but relying solely on real, scarce fracture annotations severely limits augmentation diversity. We address this by reformulating fracture synthesis as a local counterfactual editing task: generating a fracture on a healthy pelvic CT while strictly preserving the original soft tissue background. Our framework introduces a Geometric Fracture Simulation Module (GFSM) to construct explicit clinical topology priors directly from healthy bone masks, effectively decoupling the generation process from annotation scarcity. A Mask-Conditioned Latent Diffusion Model (MC-LDM) then synthesizes high-fidelity fracture CTs based on these geometric priors, optimized by a region-aware composite loss to preserve fine structural details. To ensure seamless structural integration, we employ a novel ghost-erasure and 3D Gaussian feathering pipeline. Evaluated on PENGWIN2024 and CTPelvic1k datasets, our method achieves state-of-the-art generation quality. Crucially, augmenting downstream 3D segmentation with our synthetic data improves the Dice score on Denis type-III fractures from 0.0291 to 0.6342 and reduces HD95 from 94.7068 mm to 26.3188 mm, directly demonstrating the clinical value of geometry-grounded synthesis in the long-tail trauma regime. Code and data: https://anonymous.4open.science/r/Counterfactual-Fracture-Synthesis-6DD3/