Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images. This hampers many useful applications, including pre-registered image generation, counterfactual scenarios, and others. To this end, we propose a diffusion model-based method that supports anatomically-controllable medical image generation, by following a multi-class anatomical segmentation mask at each sampling step. We additionally introduce a random mask ablation training algorithm to enable conditioning on a selected combination of anatomical constraints while allowing flexibility in other anatomical areas. We compare our model ("Seg-Diff") to existing methods on breast MRI and abdominal/neck-to-pelvis CT datasets with a wide range of anatomical objects. Results show that it reaches a new state-of-the-art in the faithfulness of generated images to input anatomical masks on both datasets, and is on par for general anatomical realism. Finally, our model also enjoys the extra benefit of being able to adjust the anatomical similarity of generated images to real images of choice through interpolation in its latent space.

翻译：扩散模型已实现高质量的医学图像生成，但在生成图像中强制执行解剖约束仍具挑战性，这阻碍了预配准图像生成、反事实场景等多种实用应用。为此，我们提出一种基于扩散模型的方法，通过在每个采样步骤中遵循多类解剖分割掩码，实现解剖可控的医学图像生成。我们进一步引入随机掩码消融训练算法，使模型能够在选定的解剖约束组合条件下进行条件生成，同时保持其他解剖区域的灵活性。我们将所提模型（"Seg-Diff"）与现有方法在涵盖广泛解剖对象的乳腺MRI和腹/颈-骨盆CT数据集上进行比较。结果表明，该模型在两个数据集的生成图像与输入解剖掩码的一致性上达到了新最优水平，并在整体解剖真实性方面与现有方法持平。最后，本模型还具备额外优势：通过潜在空间插值，可调整生成图像与选定真实图像的解剖相似度。