  Video-assisted transoral tracheal intubation (TI) necessitates using an
endoscope that helps the physician insert a tracheal tube into the glottis
instead of the esophagus. The growing trend of robotic-assisted TI would
require a medical robot to distinguish anatomical features like an experienced
physician which can be imitated by utilizing supervised deep-learning
techniques. However, the real datasets of oropharyngeal organs are often
inaccessible due to limited open-source data and patient privacy. In this work,
we propose a domain adaptive Sim-to-Real framework called IoU-Ranking
Blend-ArtFlow (IRB-AF) for image segmentation of oropharyngeal organs. The
framework includes an image blending strategy called IoU-Ranking Blend (IRB)
and style-transfer method ArtFlow. Here, IRB alleviates the problem of poor
segmentation performance caused by significant datasets domain differences;
while ArtFlow is introduced to reduce the discrepancies between datasets
further. A virtual oropharynx image dataset generated by the SOFA framework is
used as the learning subject for semantic segmentation to deal with the limited
availability of actual endoscopic images. We adapted IRB-AF with the
state-of-the-art domain adaptive segmentation models. The results demonstrate
the superior performance of our approach in further improving the segmentation
accuracy and training stability.
