Two-dimensional airway analysis using probabilistic neural networks
Junkai Tan, Bin Zheng, Sang Cheol Park, Jiantao Pu, Frank C. Sciurba, Joseph Ken Leader · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Although 3-D airway tree segmentation permits analysis of airway tree paths of practical lengths and facilitates visual inspection, our group developed and tested an automated computer scheme that was operated on individual 2-D CT images to detect airway sections and measure their morphometry and/or dimensions. The algorithm computes a set of airway features including airway lumen area (Ai), airway cross-sectional area (Aw), the ratio (Ra) of Ai to Aw, and the airway wall thickness (Tw) for each detected airway section depicted on the CT image slice. Thus, this 2-D based algorithm does not depend on the accuracy of 3-D airway tree segmentation and does not require that CT examination encompasses the entire lung or reconstructs contiguous images. However, one disadvantage of the 2-D image based schemes is the lack of the ability to identify the airway generation (Gb) of the detected airway section. In this study, we developed and tested a new approach that uses 2-D airway features to assign a generation number to an airway. We developed and tested two probabilistic neural networks (PNN) based on different sets of airway features computed by our 2-D based scheme. The PNNs were trained and tested on 12 lung CT examinations (8 training and 4 testing). The accuracy for the PNN that utilized Ai and Ra for identifying the generation of airway sections varies from 55.4% - 100%. The overall accuracy of the PNN for all detected airway sections that are spread over all generations is 76.7%. Interestingly, adding wall thickness feature (Tw) to PNN did not improve identification accuracy. This preliminary study demonstrates that a set of 2-D airway features may be used to identify the generation number of an airway with reasonable accuracy.