Two-dimensional airway analysis using probabilistic neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (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 (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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging
DOIs
StatePublished - Jun 15 2010
EventMedical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, CA, United States
Duration: Feb 14 2010Feb 16 2010

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7626
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CitySan Diego, CA
Period2/14/102/16/10

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Keywords

  • Airway detection
  • Airway generation
  • Computer-Aided Detection and Diagnosis (CAD)
  • Probabilistic Neural Network (PNN)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Tan, J., Zheng, B., Park, S. C., Pu, J., Sciurba, F. C., & Leader, J. K. (2010). Two-dimensional airway analysis using probabilistic neural networks. In Medical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging [762612] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7626). https://doi.org/10.1117/12.844497