Two-dimensional airway analysis using probabilistic neural networks

Jun Tan; Bin Zheng; Sang Cheol Park; Jiantao Pu; Frank C. Sciurba; Joseph K. Leader

doi:10.1117/12.844497

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9 March 2010 Two-dimensional airway analysis using probabilistic neural networks

Jun Tan, Bin Zheng, Sang Cheol Park, Jiantao Pu, Frank C. Sciurba, Joseph K. Leader

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Proceedings Volume 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging; 762612 (2010) https://doi.org/10.1117/12.844497
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

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 (A_i), airway cross-sectional area (A_w), the ratio (R_a) of Ai to Aw, and the airway wall thickness (T_w) 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 (G_b) 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 (T_w) 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.

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Jun Tan, Bin Zheng, Sang Cheol Park, Jiantao Pu, Frank C. Sciurba, and Joseph K. Leader "Two-dimensional airway analysis using probabilistic neural networks", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 762612 (9 March 2010); https://doi.org/10.1117/12.844497

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