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Allmendinger, Simeon ; Hemmer, Patrick ; Queisner, Moritz ; Sauer, Igor ; Müller, Leopold ; Jakubik, Johannes ; Vössing, Michael ; Kühl, Niklas:
Navigating the Synthetic Realm: Harnessing Diffusion-Based Models for Laparoscopic Text-to-Image Generation.
In: Shaban-Nejad, Arash ; Michalowski, Martin ; Bianco, Simone
(Hrsg.):
AI for Health Equity and Fairness : Leveraging AI to Address Social Determinants of Health. -
Cham
: Springer
,
2024
. - S. 31-46
ISBN 978-3-031-63592-2
DOI: https://doi.org/10.1007/978-3-031-63592-2_4
Volltext
Angaben zu Projekten
| Projektfinanzierung: |
7. Forschungsrahmenprogramm für Forschung, technologische Entwicklung und Demonstration der Europäischen Union |
|---|
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. Results on fidelity and diversity demonstrate that diffusion-based models can acquire knowledge about the style and semantics 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.