Abstract
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.
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Acknowledgements
Tianyi Zhao and Zhaozheng Yin were partially supported by National Science Foundation (NSF) CAREER award IIS-1351049.
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Zhao, T., Yin, Z., Wang, J., Gao, D., Chen, Y., Mao, Y. (2019). Bronchus Segmentation and Classification by Neural Networks and Linear Programming. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_26
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DOI: https://doi.org/10.1007/978-3-030-32226-7_26
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