@inproceedings{ca7bbe8868434eab847fb816d140a03e,
title = "Bronchus Segmentation and Classification by Neural Networks and Linear Programming",
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.",
keywords = "2D+3D neural network, Airway segmentation, Bronchus classification, Linear programming, Tracking",
author = "Tianyi Zhao and Zhaozheng Yin and Jiao Wang and Dashan Gao and Yunqiang Chen and Yunxiang Mao",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",
year = "2019",
doi = "10.1007/978-3-030-32226-7\_26",
language = "English",
isbn = "9783030322250",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "230--239",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, \{Terry M.\} and Ali Khan and Staib, \{Lawrence H.\} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
}