Abstract Pulmonary airway is the only access between the human body and the external environment, therefore the anatomy information of pulmonary airway is helpful for diagnosis of respiratory system disease. Computed tomography (CT) is one of the main methods for respiratory disease diagnosis. However, due to the large amounts of patients and images, manual reading of CT images is tedious and time-consuming. The automatic segmentation and extraction of pulmonary airway tree is the precondition of automatic analysis and computer-assisted diagnosis. Hence, according to the research progress of segmentation of pulmonary airway in recent years, we first introduced the background and the meaning of the airway segmentation. Then we analyzed the traditional methods, the method based on tube structure detection and machine learning, and the problem they met. Finally, we proposed that integrating the step of segmentation and leakage limitation can improve the accuracy and the number of branch, which means segmenting as many airways as possible at first and then eliminating the leakage.
Duan Huihong,Gong Jing,Wang Lijia, et al. A Review of Segmentation of Pulmonary Airway in Lung CT Scans[J]. Chinese Journal of Biomedical Engineering, 2018, 37(6): 739-748.
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