Segmentation-Guided Knee Radiograph Generation using Conditional
  Diffusion Models
Siyuan Mei Fuxin Fan Fabian Wagner Mareike Thies Mingxuan Gu Yipeng Sun Andreas Maier
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.