Robotic-assisted tracheal intubation requires the robot to distinguish anatomical features like an experienced physician using deep-learning techniques. However, real datasets of oropharyngeal organs are limited due to patient privacy issues, making it challenging to train deep-learning models for accurate image segmentation. We hereby consider generating a new data modality through a virtual environment to assist the training process. Specifically, this work introduces a virtual dataset generated by the Simulation Open Framework Architecture (SOFA) framework to overcome the limited availability of actual endoscopic images. We also propose a domain adaptive Sim-to-Real method for oropharyngeal organ image segmentation, which employs an image blending strategy called IoU-Ranking Blend (IRB) and style-transfer techniques to address discrepancies between datasets. Experimental results demonstrate the superior performance of the proposed approach with domain adaptive models, improving segmentation accuracy and training stability. In the practical application, the trained segmentation model holds great promise for robot-assisted intubation surgery and intelligent surgical navigation.

本文提出了一种基于虚拟环境生成口咽器官数据，采用IoU-Ranking Blend (IRB)和风格转移技术的区域自适应Sim-to-Real方法进行影像分割的方法，实验结果表明，该方法在域自适应模型上具有卓越的性能，可以提高分割精度和训练稳定性，具有广阔的机器人辅助插管手术和智能手术导航应用潜力。

机器人辅助下口腔气管插管的仿真到真实分割