用骨骼引导扩散模型，精准生成拇外翻足部X光片。
Skeleton-Guided Diffusion Model for Accurate Foot X-ray Synthesis in Hallux Valgus Diagnosis
- 引入骨骼约束的扩散模型，提升图像与解剖结构一致性。
- SSIM提升5.72%至0.794，PSNR提高18.34%达21.40 dB。
- 适合临床诊断辅助，尤其需频繁拍摄足部X光的场景。
医学图像合成在提供准确解剖图像以支持诊断和治疗中至关重要。拇外翻影响全球约19%的人口，评估常需频繁负重X光片，增加患者与医疗人员负担。现有X光合成模型在图像保真度、骨骼一致性及物理约束间难以平衡，尤其扩散模型缺乏骨骼引导。本文提出骨骼约束条件扩散模型（SCCDM），并引入基于骨骼特征点的足部评估方法KCC。SCCDM结合多尺度特征提取与注意力机制，使结构相似性指数（SSIM）提升5.72%（达0.794），峰值信噪比（PSNR）提升18.34%（达21.40 dB）。与KCC联合使用时，平均得分达0.85，体现良好临床适用性。代码已公开于https://github.com/midisec/SCCDM。
原文摘要 · Abstract (English)
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.
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