Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts.
Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts.
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DOI：
10.1109/tmi.2014.2374615
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发表时间：
2015-05
影响因子：
10.6
通讯作者：
Beichel RR
中科院分区：
文献类型：
--
作者：
Bauer C;Eberlein M;Beichel RR
我们提出了一个基于图的框架，用于从CT扫描中重建气道树，并评估了不同特征类别及其组合在五个肺部队列上的性能。该方法包括两个主要处理步骤。首先，识别潜在的气道分支和连接候选对象，并分别用带有加权节点和边的图结构来表示。其次，利用一种优化算法，根据从图像特征得出的图权重选择气道分支和连接的一个子集，从而生成气道检测结果。在来自五个不同队列（包括正常和患病肺部）的50例肺部CT扫描上评估了具有不同特征类别及其组合的算法性能。结果显示了在正确（真阳性）和错误（假阳性）识别气道方面特征类别/组合之间的权衡。此外，还分析了特征性能对肺部队列的依赖性。在所有队列中，通过灰度值、局部形状和结构特征的组合，实现了高真阳性率（TPR）和低假阳性率（FPR）之间的良好权衡。这种组合能够提取91.80%的参考气道（TPR），同时假阳性率（FPR）低至1.00%。此外，在公开的EXACT’09测试集上对该变体进行了评估，并与其他气道检测方法进行了比较。所提出方法的主要优点之一是它对肺部CT扫描中经常出现的局部干扰/伪影或其他模糊情况具有鲁棒性。
We present a graph-based framework for airway tree reconstruction from CT scans and evaluate the performance of different feature categories and their combinations on five lung cohorts. The approach consists of two main processing steps. First, potential airway branch and connection candidates are identified and represented by a graph structure with weighted nodes and edges, respectively. Second, an optimization algorithm is utilized for generating an airway detection result by selecting a subset of airway branches and connections based on graph weights derived from image features. The performance of the algorithm with different feature categories and their combinations was assessed on a set of 50 lung CT scans from five different cohorts, including normal and diseased lungs. Results show tradeoffs between feature categories/combinations in terms of correctly (true positive) and incorrectly (false positive) identified airways. Also, the performance of features in dependence of lung cohort was analyzed. Across all cohorts, a good trade-off with high true positive rate (TPR) and low false positive rate (FPR) was achieved by a combination of gray-value, local shape, and structural features. This combination enabled extracting 91.80% of reference airways (TPR) in combination with a low FPR of 1.00%. In addition, this variant was evaluated on the public EXACT’09 test set, and a comparison with other airway detection approaches is provided. One of the main advantages of the presented method is that it is robust against local disturbances/artifacts or other ambiguities that are frequently occurring in lung CT scans.