Edge-Based Airway Boundary Extraction from Endotracheal Endoscopic Images

doi:10.11871/jfdc.issn.2096-742X.2026.05.003

Abstract

Abstract:

[Objective] This study aims to develop a vision-based method for continuous and quantitative monitoring of bronchial cuff position in double-lumen endotracheal tubes during thoracic surgery, where cuff displacement may compromise ventilation and increase perioperative complications. [Methods] Airway images acquired from an integrated camera mounted on a visualized double-lumen endotracheal tube are processed using image enhancement techniques to improve boundary visibility. Airway boundaries are extracted using both a multiscale edge detection method and a U-Net segmentation model. The spatial relationship between the tube and airway wall is quantitatively evaluated using a medial axis transform (MAT)-based distance measurement algorithm. The performance of the two boundary extraction methods and the real-time capability of the system are experimentally evaluated using images extracted from live video. [Results] The multiscale edge detection method successfully identifies airway boundaries without requiring annotated training data and exhibits low computational cost. In contrast, the U-Net model produces more continuous, robust, and accurate boundary representations, providing superior performance for geometric measurement and quantitative analysis. After model quantization, the U-Net model achieves a compact size of 16 MB and a processing speed of 4 FPS on a standard CPU platform. [Conclusions] Although multiscale edge detection offer advantages in computational efficiency and ease of deployment, the U-Net-based framework demonstrates superior boundary extraction accuracy, robustness, and quantitative analysis capability. Combined with MAT-based distance measurement, the proposed method provides a reliable solution for real-time bronchial cuff position monitoring and quantitative airway assessment during thoracic surgery.

Key words: visualized double-lumen endotracheal tube, deep learning, image analysis

WANG Bo, XU Yucan, GAO Jihan, SHE Weiguang, LIANG Cunxia, JIN Bing, WANG Xinchun, ZHOU Yancong, SUN Bao. Edge-Based Airway Boundary Extraction from Endotracheal Endoscopic Images[J]. Frontiers of Data and Computing, 2026, 8(5): 34-47, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.05.003.

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