Bronchus Segmentation and Classification by Neural Networks and Linear Programming

  • Tianyi Zhao
  • Zhaozheng YinEmail author
  • Jiao Wang
  • Dashan Gao
  • Yunqiang Chen
  • Yunxiang Mao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


Airway segmentation is a critical problem for lung disease analysis. However, building a complete airway tree is still a challenging problem because of the complex tree structure, and tracing the deep bronchi is not trivial in CT images because there are numerous small airways with various directions. In this paper, we develop two-stage 2D+3D neural networks and a linear programming based tracking algorithm for airway segmentation. Furthermore, we propose a bronchus classification algorithm based on the segmentation results. Our algorithm is evaluated on a dataset collected from 4 resources. We achieved the dice coefficient of 0.94 and F1 score of 0.86 by a centerline based evaluation metric, compared to the ground-truth manually labeled by our radiologists.


Airway segmentation 2D+3D neural network Linear programming Tracking Bronchus classification 



Tianyi Zhao and Zhaozheng Yin were partially supported by National Science Foundation (NSF) CAREER award IIS-1351049.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tianyi Zhao
    • 1
    • 2
  • Zhaozheng Yin
    • 1
    Email author
  • Jiao Wang
    • 2
  • Dashan Gao
    • 2
  • Yunqiang Chen
    • 2
  • Yunxiang Mao
    • 2
  1. 1.Missouri University of Science and TechnologyRollaUSA
  2. 2.12 Sigma TechnologiesSan DiegoUSA

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