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.Comment: Extended abstract in IEEE ICRA 2023 Workshop (New Evolutions in
Surgical Robotics: Embracing Multimodal Imaging Guidance, Intelligence, and
Bio-inspired Mechanisms