Bronchus Segmentation and Classification by Neural Networks and Linear Programming
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.
T. Zhao et al., "Bronchus Segmentation and Classification by Neural Networks and Linear Programming," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11769 LNCS, pp. 230-239, Springer, Oct 2019.
The definitive version is available at https://doi.org/10.1007/978-3-030-32226-7_26
22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019: Medical Image Computing and Computer Assisted Intervention (2019: Oct. 13-17, Shenzhen, China)
Keywords and Phrases
2D+3D neural network; Airway segmentation; Bronchus classification; Linear programming; Tracking
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Oct 2019