Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

中文翻译：

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医学图像翻译的级联多路径快捷扩散模型


图像到图像的转换是医学成像处理的重要组成部分，在各种成像模式和临床场景中都有许多用途。以前的方法包括生成对抗网络（GAN）和扩散模型（DM），它们提供了现实性，但存在不稳定且缺乏不确定性估计的问题。尽管 GAN 和 DM 方法都在医学图像翻译任务中各自展示了各自的能力，但将 GAN 和 DM 结合起来以进一步提高翻译性能并实现不确定性估计的潜力仍然很大程度上尚未被开发。在这项工作中，我们通过提出用于高质量医学图像转换和不确定性估计的级联多路径快捷扩散模型（CMDM）来应对这些挑战。为了减少所需的迭代次数并确保稳健的性能，我们的方法首先获得条件 GAN 生成的先验图像，该图像将在后续步骤中使用 DM 进行有效的反向翻译。此外，采用多路径捷径扩散策略来细化翻译结果并估计不确定性。级联管道进一步提高了翻译质量，结合了级联之间的残差平均。我们收集了三个不同的医学图像数据集，每个数据集有两个子任务，以测试我们方法的普遍性。我们的实验结果发现，CMDM 可以产生与最先进的方法相当的高质量翻译，同时提供与翻译错误良好相关的合理的不确定性估计。