Generating healthy counterfactuals from pathological images holds significant promise in medical imaging, e.g., in anomaly detection or for application of analysis tools that are designed for healthy scans. These counterfactuals should represent what a patient's scan would plausibly look like in the absence of pathology, preserving individual anatomical characteristics while modifying only the pathological regions. Denoising diffusion probabilistic models (DDPMs) have become popular methods for generating healthy counterfactuals of pathology data. Typically, this involves training on solely healthy data with the assumption that a partial denoising process will be unable to model disease regions and will instead reconstruct a closely matched healthy counterpart. More recent methods have incorporated synthetic pathological images to better guide the diffusion process. However, it remains challenging to guide the generative process in a way that effectively balances the removal of anomalies with the retention of subject-specific features. To solve this problem, we propose a novel application of denoising diffusion bridge models (DDBMs) - which, unlike DDPMs, condition the diffusion process not only on the initial point (i.e., the healthy image), but also on the final point (i.e., a corresponding synthetically generated pathological image). Treating the pathological image as a structurally informative prior enables us to generate counterfactuals that closely match the patient's anatomy while selectively removing pathology. The results show that our DDBM outperforms previously proposed diffusion models and fully supervised approaches at segmentation and anomaly detection tasks.

翻译：从病理图像生成健康反事实图像在医学成像领域具有重要前景，例如在异常检测或适用于健康扫描设计的分析工具应用中。这些反事实图像应能合理呈现患者在没有病理情况下的扫描结果，在仅修改病理区域的同时保留个体解剖特征。去噪扩散概率模型已成为生成病理数据健康反事实图像的常用方法。典型方法是在仅使用健康数据训练的基础上，假设部分去噪过程无法对疾病区域建模，从而重建高度匹配的健康对应图像。近期方法通过引入合成病理图像以更好地引导扩散过程。然而，如何在有效消除异常与保留受试者特异性特征之间实现平衡，仍是引导生成过程面临的挑战。为解决该问题，我们提出去噪扩散桥模型的新颖应用——与DDPM不同，该模型不仅以初始点（即健康图像）为条件，同时以终点（即对应生成的合成病理图像）为条件进行扩散过程建模。将病理图像作为结构信息先验，使我们能够生成与患者解剖结构高度匹配、同时选择性消除病理特征的反事实图像。实验结果表明，在分割和异常检测任务中，我们的DDBM模型性能优于先前提出的扩散模型及全监督方法。