Recent advances in synthetic imaging open up opportunities for obtaining
additional data in the field of surgical imaging. This data can provide
reliable supplements supporting surgical applications and decision-making
through computer vision. Particularly the field of image-guided surgery, such
as laparoscopic and robotic-assisted surgery, benefits strongly from synthetic
image datasets and virtual surgical training methods. Our study presents an
intuitive approach for generating synthetic laparoscopic images from short text
prompts using diffusion-based generative models. We demonstrate the usage of
state-of-the-art text-to-image architectures in the context of laparoscopic
imaging with regard to the surgical removal of the gallbladder as an example.
Results on fidelity and diversity demonstrate that diffusion-based models can
acquire knowledge about the style and semantics in the field of image-guided
surgery. A validation study with a human assessment survey underlines the
realistic nature of our synthetic data, as medical personnel detects actual
images in a pool with generated images causing a false-positive rate of 66%. In
addition, the investigation of a state-of-the-art machine learning model to
recognize surgical actions indicates enhanced results when trained with
additional generated images of up to 5.20%. Overall, the achieved image quality
contributes to the usage of computer-generated images in surgical applications
and enhances its path to maturity.

通过扩散生成模型，我们提出了一种直观的方法来从短文本提示生成合成的腹腔镜图像，采用最先进的文本到图像架构，在图像引导手术领域，特别是腹腔镜和机器人辅助手术方面，得到了依靠合成图像数据和虚拟手术训练方法的强力帮助。