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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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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.

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Authors and Affiliations

  1. Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Qier Meng, Masahiro Oda & Kensaku Mori

  2. Graduate School of Information Science, Aichi Institute of Technology, 1247 Yachigusa, Yagusa-cho, Toyota, Aichi, Japan

    Takayuki Kitasaka

  3. Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Yukitaka Nimura & Kensaku Mori

  4. Department of Diagnostic Radiology, Graduate School of Health Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan

    Junji Ueno

Authors
  1. Qier Meng
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  2. Takayuki Kitasaka
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  3. Yukitaka Nimura
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  4. Masahiro Oda
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  6. Kensaku Mori
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Correspondence to Qier Meng.

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The authors declare that they have no conflict of interest.

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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.

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

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Meng, Q., Kitasaka, T., Nimura, Y. et al. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume. Int J CARS 12, 245–261 (2017). https://doi.org/10.1007/s11548-016-1492-2

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  • DOI: https://doi.org/10.1007/s11548-016-1492-2

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