Navigating the Synthetic Realm: Harnessing Diffusion-based Models for
  Laparoscopic Text-to-Image Generation
Simeon Allmendinger Patrick Hemmer Moritz Queisner Igor Sauer Leopold Müller Johannes Jakubik Michael Vössing Niklas Kühl
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.