Markerless gating for lung cancer radiotherapy based on machine learning techniques
Markerless gating for lung cancer radiotherapy based on machine learning techniques
复制标题
基于机器学习技术的肺癌放疗无标记门控
DOI：
10.1088/0031-9155/54/6/010
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发表时间：
2009-03-01
影响因子：
3.5
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Lin, Tong;Li, Ruijiang;Jiang, Steve B.
在肺癌放疗中，可以通过呼吸门控将辐射传递给移动靶标，在治疗过程中的任何时间点，我们需要知道靶标是在预定义的门控窗口内还是外。这可以通过跟踪植入目标内部或附近的一个或多个基准标记来实现，无论是透视还是电磁。然而，标志物追踪在肺癌放疗中的临床实施受到限制，主要是由于气胸的风险。因此，不植入基准标志物的门控是一个很有前途的临床方向。我们已经开发了几种模板匹配方法，用于透视无标记门控。最近，我们将门控问题建模为一个二元模式分类问题，其中主成分分析（PCA）和支持向量机（SVM）相结合来执行分类任务。在相同的框架下，我们研究了降维技术（PCA和四种非线性流形学习方法）和两种机器学习分类方法（人工神经网络- ann和支持向量机）的不同组合。对9例肺癌患者的10组透视图像序列进行了性能评价。我们发现，在所有降维技术和分类方法的组合中，PCA与ANN或SVM的组合都比其他非线性流形学习方法取得了更好的性能。尽管两种分类方法的目标覆盖率相似，但当ANN与PCA结合使用时，在分类准确率和召回率方面都优于SVM。此外，基于PCA的ANN和SVM的运行时间都在实时应用的允许范围内。总的来说，人工神经网络与主成分分析相结合是实时门控放疗的最佳选择。
In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.