Domain adaptive Sim-to-Real segmentation of oropharyngeal organs

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 p...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2023-10, Vol.61 (10), p.2745-2755
Main Authors: Wang, Guankun, Ren, Tian-Ao, Lai, Jiewen, Bai, Long, Ren, Hongliang
Format: Article
Language:English
Subjects:
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Applications
Datasets
Deep learning
Endoscopes
Esophagus
Glottis
Human Physiology
Image processing
Image segmentation
Imaging
Original Article
Oropharynx
Physicians
Radiology
Ranking
Semantic segmentation
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Summary: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 I oU- R anking B lend- A rt F low (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. Graphical abstract
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-023-02877-0