Segmentation of the airway tree from chest ct using tiny atrous convolutional network

Abstract

The airway tree is one of the most important part in human respiratory system. Airway segmentation plays a crucial role in pulmonary disease diagnosis, localization and surgical navigation. We propose a novel method to improve airway segmentation in thoracic computed tomography(CT) using deep learning. In order to take into account the multi-scale changes of the airway and achieve accurate airway segmentation, we design an end-to-end Tiny Atrous Convolutional Network (TACNet) based on 3D convolution neural network. In view of the difficulty of classification due to the numerous branches of airway, two evaluation factors, namely, the angle of airway bifurcation and the buffer length of airway bifurcation are designed, which are used for airway classification by combining the centerline extraction. We train TACNet on the inspirator thoracic CT scans with ground truth, which are generated by clinicians and evaluate on our own clinical data sets and EXACT'09 data sets. Compared with state-of-the-art airway segmentation algorithms, proposed algorithm in this paper achieve very competitive results in 20 test datasets of the EXACT'09 challenge. The experimental results show that the algorithm proposed in this paper has high robustness and advantages regardless of airway segmentation or airway classification. In 20 test datasets of the EXACT'09 challenge, the average airway tree length detection rate is the best in the public literatures.