Skeleton-Guided Diffusion Model for Accurate Foot X-ray Synthesis in Hallux Valgus Diagnosis
This work addresses the need for accurate medical image synthesis to reduce strain on patients and healthcare providers in diagnosing hallux valgus, which affects about 19% of the population, but it is incremental as it builds on existing diffusion-based methods with skeletal guidance.
The paper tackled the problem of generating anatomically accurate foot X-rays for hallux valgus diagnosis by proposing a skeletal-constrained diffusion model, which improved SSIM by 5.72% to 0.794 and PSNR by 18.34% to 21.40 dB, achieving a clinical applicability score of 0.85.
Medical image synthesis plays a crucial role in providing anatomically accurate images for diagnosis and treatment. Hallux valgus, which affects approximately 19% of the global population, requires frequent weight-bearing X-rays for assessment, placing additional strain on both patients and healthcare providers. Existing X-ray models often struggle to balance image fidelity, skeletal consistency, and physical constraints, particularly in diffusion-based methods that lack skeletal guidance. We propose the Skeletal-Constrained Conditional Diffusion Model (SCCDM) and introduce KCC, a foot evaluation method utilizing skeletal landmarks. SCCDM incorporates multi-scale feature extraction and attention mechanisms, improving the Structural Similarity Index (SSIM) by 5.72% (0.794) and Peak Signal-to-Noise Ratio (PSNR) by 18.34% (21.40 dB). When combined with KCC, the model achieves an average score of 0.85, demonstrating strong clinical applicability. The code is available at https://github.com/midisec/SCCDM.