Navigating the Synthetic Realm: Harnessing Diffusion-based Models for
Laparoscopic Text-to-Image Generation
- URL: http://arxiv.org/abs/2312.03043v1
- Date: Tue, 5 Dec 2023 16:20:22 GMT
- Title: Navigating the Synthetic Realm: Harnessing Diffusion-based Models for
Laparoscopic Text-to-Image Generation
- Authors: Simeon Allmendinger, Patrick Hemmer, Moritz Queisner, Igor Sauer,
Leopold M\"uller, Johannes Jakubik, Michael V\"ossing, Niklas K\"uhl
- Abstract summary: We present an intuitive approach for generating synthetic laparoscopic images from short text prompts using diffusion-based generative models.
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.
- Score: 3.2039076408339353
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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