Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy
Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy
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DOI：
10.1118/1.4906197
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发表时间：
2015-02-01
期刊：
影响因子：
3.8
通讯作者：
Li, Hua
中科院分区：
文献类型：
--
作者：
Chen, Hsin-Chen;Tan, Jun;Li, Hua
目的：放射治疗中最关键的步骤之一是精确的肿瘤和关键器官危险（OAR）轮廓。手动和自动轮廓过程都容易出错，并且在很大程度上存在观察者之间和内部的可变性。这通常是由于成像技术在可视化人体解剖学方面的局限性以及个体之间固有的解剖学变异性。医生/物理学家在制定治疗计划之前，必须对每个病人的所有放射治疗轮廓进行复核，这是一个繁琐、费力的过程，而且仍然不是一个没有错误的过程。在这项研究中，作者开发了一种基于新型几何属性分布（GAD）模型的通用策略，以自动检测放射治疗OAR轮廓误差，并简化当前的临床工作流程。方法：考虑放射治疗结构的几何属性（质心、体积和形状）、相邻结构的空间关系以及患者个体轮廓的解剖相似性，建立GAD模型来表征每个个体结构的结构间质心和体积变化以及结构内形状变化。GAD模型是可伸缩和可变形的，并且受各自的主属性变化的约束，这些变化是从经过验证的桨形轮廓的训练集计算出来的。提出了一种新的迭代加权GAD模型拟合算法，用于轮廓误差检测。采用独特的受试者工作特征（ROC）分析方法优化模型参数以满足临床需求。共有44例头颈部患者病例，其中每个病例包括9个关键桨形轮廓，用于演示拟议的策略。这44例患者中有29例用于训练结构间和结构内广泛性焦虑症模型。这些训练数据和其余15个测试数据集分别用于测试所提出的轮廓错误检测策略的有效性。结果：实现了一个评估工具来说明所提出的策略如何自动检测给定患者的放射治疗轮廓错误，并提供错误检测结果的3D图形可视化。轮廓误差检测结果的平均灵敏度为0.954/0.906，平均特异性为0.901/0.909。对于结构形状相关轮廓误差的检测结果，所有被测样品的平均灵敏度为0.816，平均特异性为0.94。结果表明，该方法具有较低的误检率和轮廓误差检测的可行性。结论：所提出的策略可以可靠地识别基于临床批准的轮廓的内部和内部结构约束的轮廓错误。它在改善放射治疗工作流程方面具有很大的潜力。ROC和箱形图分析允许对系统参数进行分析调整以满足临床要求。未来的工作将侧重于通过使用更多的训练集和附加的几何属性约束来提高策略的可靠性。(C) 2015年美国医学物理学家协会。
Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter-and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow.Methods: Considering the radiation therapy structures' geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter-and intrastructural GAD models. These training data and the remaining fifteen testing data sets were separately employed to test the effectiveness of the proposed contouring error detection strategy.Results: An evaluation tool was implemented to illustrate how the proposed strategy automatically detects the radiation therapy contouring errors for a given patient and provides 3D graphical visualization of error detection results as well. The contouring error detection results were achieved with an average sensitivity of 0.954/0.906 and an average specificity of 0.901/0.909 on the centroid/volume related contouring errors of all the tested samples. As for the detection results on structural shape related contouring errors, an average sensitivity of 0.816 and an average specificity of 0.94 on all the tested samples were obtained. The promising results indicated the feasibility of the proposed strategy for the detection of contouring errors with low false detection rate.Conclusions: The proposed strategy can reliably identify contouring errors based upon inter-and intrastructural constraints derived from clinically approved contours. It holds great potential for improving the radiation therapy workflow. ROC and box plot analyses allow for analytically tuning of the system parameters to satisfy clinical requirements. Future work will focus on the improvement of strategy reliability by utilizing more training sets and additional geometric attribute constraints. (C) 2015 American Association of Physicists in Medicine.