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.