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.

翻译：机器人辅助气管插管要求机器人像经验丰富的医生一样，利用深度学习技术区分解剖特征。然而，由于患者隐私问题，口咽器官的真实数据集十分有限，这使得训练用于精确图像分割的深度学习模型面临挑战。为此，我们考虑通过虚拟环境生成一种新的数据模态来辅助训练过程。具体而言，本研究引入了一种由仿真开放框架架构（SOFA）生成的虚拟数据集，以克服真实内窥镜图像数据稀缺的问题。我们还提出了一种面向口咽器官图像分割的域自适应仿真到真实方法，该方法采用名为IOU排序混合（IRB）的图像混合策略以及风格迁移技术来应对数据集之间的差异。实验结果表明，所提方法在域自适应模型上展现出卓越性能，提升了分割精度和训练稳定性。在实际应用中，训练好的分割模型在机器人辅助插管手术和智能手术导航方面具有广阔前景。