Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models
This work addresses data scarcity in medical imaging for researchers and developers, but it is incremental as it applies existing conditional diffusion techniques to a specific domain.
The authors tackled the problem of limited surgical data for deep learning in medical imaging by generating synthetic knee radiographs using conditional diffusion models guided by segmentation maps, achieving realistic image generation that adheres to the conditioning segmentation with the conditional training method outperforming alternatives.
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.