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Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume

  • Qier MengEmail author
  • Takayuki Kitasaka
  • Yukitaka Nimura
  • Masahiro Oda
  • Junji Ueno
  • Kensaku Mori
Original Article

Abstract

Purpose

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.

Methods

This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.

Results

A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.

Conclusion

A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Keywords

Hessian matrix analysis Adaptive multiscale cavity enhancement filter Support vector machine Graph-cut 

Notes

Acknowledgments

The authors thank our colleagues for suggestions and advice. Parts of this research were supported by the MEXT, the JSPS KAKENHI Grant Numbers 25242047, 26108006, 26560255, and the Kayamori Foundation of Informational Science Advancement.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2016

Authors and Affiliations

  • Qier Meng
    • 1
    Email author
  • Takayuki Kitasaka
    • 2
  • Yukitaka Nimura
    • 3
  • Masahiro Oda
    • 1
  • Junji Ueno
    • 4
  • Kensaku Mori
    • 1
    • 3
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Graduate School of Information ScienceAichi Institute of TechnologyToyotaJapan
  3. 3.Information Strategy Office, Information and CommunicationsNagoya UniversityNagoyaJapan
  4. 4.Department of Diagnostic Radiology, Graduate School of Health SciencesTokushima UniversityTokushimaJapan

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