用双扩散模型生成肝部MRI和分割标签，提升3D U-Net分割效果
FOSCU: Feasibility of Synthetic MRI Generation via Duo-Diffusion Models for Enhancement of 3D U-Nets in Hepatic Segmentation
- 用带控制网的3D扩散模型同步生成逼真MRI和对应标签
- 合成数据训练使平均Dice提升0.67%，图像质量改善36.4%
- 适合缺乏标注数据的医学影像分割研究者使用
医学图像分割面临数据获取受限、标注成本高、临床数据不足等挑战，这些系统性障碍严重制约了鲁棒分割算法的发展。为应对这些问题，我们提出FOSCU，融合双扩散模型（Duo-Diffusion）与增强的3D U-Net训练流程。Duo-Diffusion是一种3D潜在扩散模型，结合ControlNet，能同步生成高分辨率、解剖结构真实的合成MRI体积及对应的分割标签；其通过分割条件扩散机制确保生成数据的空间一致性与精确解剖细节。在720例腹部MRI扫描上的实验表明，使用真实与合成数据联合训练的模型相比仅用真实数据的模型，平均Dice得分提升0.67%，弗雷歇起始距离（FID）降低36.4%，反映图像保真度显著提升。
原文摘要 · Abstract (English)
Medical image segmentation faces fundamental challenges including restricted access, costly annotation, and data shortage to clinical datasets through Picture Archiving and Communication Systems (PACS). These systemic barriers significantly impede the development of robust segmentation algorithms. To address these challenges, we propose FOSCU, which integrates Duo-Diffusion, a 3D latent diffusion model with ControlNet that simultaneously generates high-resolution, anatomically realistic synthetic MRI volumes and corresponding segmentation labels, and an enhanced 3D U-Net training pipeline. Duo-Diffusion employs segmentation-conditioned diffusion to ensure spatial consistency and precise anatomical detail in the generated data. Experimental evaluation on 720 abdominal MRI scans shows that models trained with combined real and synthetic data yield a mean Dice score gain of 0.67% over those using only real data, and achieve a 36.4% reduction in Fréchet Inception Distance (FID), reflecting enhanced image fidelity.
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