  As a new emerging and promising type of generative models, diffusion models
have proven to outperform Generative Adversarial Networks (GANs) in multiple
tasks, including image synthesis. In this work, we explore semantic image
synthesis for abdominal CT using conditional diffusion models, which can be
used for downstream applications such as data augmentation. We systematically
evaluated the performance of three diffusion models, as well as to other
state-of-the-art GAN-based approaches, and studied the different conditioning
scenarios for the semantic mask. Experimental results demonstrated that
diffusion models were able to synthesize abdominal CT images with better
quality. Additionally, encoding the mask and the input separately is more
effective than na\"ive concatenating.
