Deep learning-based medical image processing algorithms require representative data during development. In particular, surgical data might be difficult to obtain, and high-quality public datasets are limited. To overcome this limitation and augment datasets, a widely adopted solution is the generation of synthetic images. In this work, we employ conditional diffusion models to generate knee radiographs from contour and bone segmentations. Remarkably, two distinct strategies are presented by incorporating the segmentation as a condition into the sampling and training process, namely, conditional sampling and conditional training. The results demonstrate that both methods can generate realistic images while adhering to the conditioning segmentation. The conditional training method outperforms the conditional sampling method and the conventional U-Net.

翻译：基于深度学习的医学图像处理算法在开发过程中需要具有代表性的数据。特别是手术数据可能难以获取，且高质量公共数据集十分有限。为解决这一局限并扩充数据集，合成图像生成已成为广泛采用的方案。本研究采用条件扩散模型，从轮廓与骨骼分割结果生成膝关节X光片。值得注意的是，我们提出了两种将分割结果作为条件纳入采样与训练过程的策略，即条件采样与条件训练。结果表明，两种方法均能在遵循条件分割的前提下生成逼真的图像。其中，条件训练方法在性能上优于条件采样方法及传统U-Net模型。