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，通过扩充小型或不平衡数据集等方式，有助于降低获取和标注新图像的成本，并支持其他应用。然而，这类模型在确保生成图像的整体解剖真实性方面仍面临挑战。为此，我们提出了一种用于解剖可控医学图像生成的扩散模型。该模型在每个采样步骤中遵循多类解剖分割掩码，并引入了一种随机掩码消融训练算法，从而允许在解剖约束的选定组合条件下生成图像，同时保持其他解剖区域的灵活性。这一方法还提升了网络在完全无约束（无条件生成）情况下对解剖真实性的学习能力。基于乳腺MRI和腹部/颈部到骨盆CT数据集的对比评估表明，与现有最优模型相比，本模型在解剖真实性和对输入掩码的忠实度方面均表现更优。我们还提供了易于使用的代码库，并发布了一组生成的配对乳腺MRI数据集。本方法可支持多种应用，包括预配准图像生成、反事实场景等。