Diffusion models have enabled remarkably high-quality medical image generation, which can help mitigate the expenses of acquiring and annotating new images by supplementing small or imbalanced datasets, along with other applications. However, these are hampered by the challenge of enforcing global anatomical realism in generated images. To this end, we propose a diffusion model for anatomically-controlled medical image generation. Our model follows a multi-class anatomical segmentation mask at each sampling step and incorporates a \textit{random mask ablation} training algorithm, to enable conditioning on a selected combination of anatomical constraints while allowing flexibility in other anatomical areas. This also improves the network's learning of anatomical realism for the completely unconditional (unconstrained generation) case. Comparative evaluation on breast MRI and abdominal/neck-to-pelvis CT datasets demonstrates superior anatomical realism and input mask faithfulness over state-of-the-art models. We also offer an accessible codebase and release a dataset of generated paired breast MRIs. Our approach facilitates diverse applications, including pre-registered image generation, counterfactual scenarios, and others.

翻译：扩散模型已能生成质量极高的医学图像，通过补充小型或不平衡数据集等应用，有助于缓解获取和标注新图像的高昂成本。然而，这类模型在确保生成图像的整体解剖学真实性方面仍面临挑战。为此，我们提出一种基于解剖可控性的医学图像生成扩散模型。该模型在每个采样步骤中遵循多类解剖分割掩码，并采用随机掩码消融训练算法，从而在允许其他解剖区域灵活性的同时，实现对选定解剖约束组合的条件控制。该方法还能提升网络在完全无约束（无条件生成）情况下对解剖真实性的学习能力。在乳腺MRI和腹部/颈部至骨盆CT数据集上的对比评估表明，本模型在解剖真实性和输入掩码忠实度方面均优于现有最优模型。我们还提供了可访问的代码库，并公开了一组配对乳腺MRI生成数据集。我们的方法可支持预配准图像生成、反事实场景等多样化应用。