This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed to identify the four key bifurcation points. Then, based on the detected bifurcation points, the entire airway tree is reconstructed by a new region-growing method. Finally, with the basic airway tree anatomy and topology knowledge, individual branches of the airway tree are classified into different categories in terms of pulmonary lobes. There are several advantages in our method such as the detection of the bifurcation points does not depend on the segmentation of airway tree and only four bifurcation points need to be manually labeled for each sample to prepare the training dataset. The segmentation of airway tree is guided by the detected points, which overcomes the difficulty of manual seed selection of conventional region-growing algorithm. In addition, the bifurcation points can help analyze the tree structure, which provides a basis for effective airway tree labeling. Experimental results show that our method is fast, stable, and the accuracy of our method is 97.85%, which is higher than that of the traditional skeleton-based method.

The pipeline of our proposed lobe-based airway tree labeling method. Given a raw CT volume, a neural network structure is designed to predict major bifurcation points of airway tree. Based on the detected points, airway tree is reconstructed and labeled in terms of lobes

本文提出一种基于叶的气道树自动标注方法，该方法可以从计算机断层扫描图像中检测出分叉点，以重构和标注气道树。设计了一种基于深度学习的网络结构，以识别四个关键分支点。然后，基于检测到的分叉点，通过新的区域增长方法重建整个气道树。最后，借助基本的气道树解剖结构和拓扑知识，根据肺叶将气道树的各个分支分为不同的类别。我们的方法有几个优点，例如，分叉点的检测不依赖于气道树的分割，每个样本只需要手动标记四个分叉点即可准备训练数据集。气管树的分割以检测到的点为指导，克服了传统的区域生长算法人工选择种子的难题。另外，分叉点可以帮助分析树的结构，这为有效的气道树标记提供了基础。实验结果表明，该方法快速，稳定，准确度达到97.85％，高于传统的基于骨架的方法。

我们提出的基于叶的气道树标记方法的管道。给定原始CT体积，将设计神经网络结构来预测气道树的主要分叉点。基于检测到的点，对气道树进行重建并根据裂片进行标记