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。

航行合成领域：利用扩散模型进行腹腔镜文本到图像生成

Navigating the Synthetic Realm: Harnessing Diffusion-based Models for  Laparoscopic Text-to-Image Generation

A significant challenge in image-guided surgery is the accurate measurement
task of relevant structures such as vessel segments, resection margins, or
bowel lengths. While this task is an essential component of many surgeries, it
involves substantial human effort and is prone to inaccuracies. In this paper,
we develop a novel human-AI-based method for laparoscopic measurements
utilizing stereo vision that has been guided by practicing surgeons. Based on a
holistic qualitative requirements analysis, this work proposes a comprehensive
measurement method, which comprises state-of-the-art machine learning
architectures, such as RAFT-Stereo and YOLOv8. The developed method is assessed
in various realistic experimental evaluation environments. Our results outline
the potential of our method achieving high accuracies in distance measurements
with errors below 1 mm. Furthermore, on-surface measurements demonstrate
robustness when applied in challenging environments with textureless regions.
Overall, by addressing the inherent challenges of image-guided surgery, we lay
the foundation for a more robust and accurate solution for intra- and
postoperative measurements, enabling more precise, safe, and efficient surgical
procedures.

基于医生实践指导的立体视觉和机器学习方法，该研究提出一种用于图像引导手术的新型人工智能辅助的腹腔镜测量方法，通过在实验环境中的评估，得到高精度的距离测量，并在具有无纹理区域的挑战性环境中表现出稳健性，为术中和术后测量提供了更精确、安全和高效的解决方案的基础。