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NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline for Planning of Navigation Bronchoscopy

preprint
posted on 03.03.2022, 03:57 by Andong WangAndong Wang, Terrence Chi Chun Tam, Ho Ming Poon, Kun-Chang Yu, Wei-Ning LeeWei-Ning Lee
Navigation bronchoscopy is a minimally invasive procedure in which doctors pass a bronchoscope into a subject’s airways to sample the target pulmonary lesion. A three-dimensional (3D) airway roadmap reconstructed from Computer Tomography (CT) scans is a prerequisite for this procedure, especially when the target is distally located. Therefore, an accurate and efficient airway segmentation algorithm is essential to reduce bronchoscopists’ burden of pre-procedural airway identification as well as patients’ discomfort during the prolonged procedure. However, airway segmentation remains a challenging task because of the intrinsic complex tree-like structure, imbalanced sizes of airway branches, potential domain shifts of CT scans, and few available labeled images. To address these problems, we present a deep learning-based pipeline, denoted as NaviAirway, which finds finer bronchioles through four major novel components – feature extractor modules in model architecture design, a bronchiole-sensitive loss function, a human-vision-inspired iterative training strategy, and a semi-supervised learning framework to utilize unlabeled CT images. Experimental results showed that NaviAirway outperformed existing methods, particularly in identification of higher generation bronchioles and robustness to new CT scans. On average, NaviAirway takes five minutes to segment the CT scans of one patient on a GPU-embedded computer. Moreover, we propose two new metrics to complement conventional ones for a more comprehensive and fairer evaluation of deep learning-based airway segmentation approaches. The code is publicly available on https://github.com/AntonotnaWang/NaviAirway.

Funding

COVID-19 Action Seed Funding of Faculty of Engineering, The University of Hong Kong

History

Email Address of Submitting Author

wangad@connect.hku.hk

Submitting Author's Institution

The University of Hong Kong

Submitting Author's Country

Hong Kong