Abstract
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.
Abstract (translated)
基于深度学习的医疗图像处理算法在开发过程中需要代表性数据。特别是，手术数据可能很难获得，高质量的公共数据集有限。要克服这一局限并扩展数据集，一种广泛采用的解决方案是生成合成图像。在这项工作中，我们使用条件扩散模型根据轮廓和骨分割生成膝关节X线。值得注意的是，将分割作为一个条件融入抽样和训练过程中提出了两种不同的策略，即条件抽样和条件训练。结果显示，两种方法都可以生成逼真的图像，同时满足条件分割。条件训练方法超越了条件抽样方法和传统的U-Net。
URL
https://arxiv.org/abs/2404.03541
