Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning
Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning
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DOI：
10.1109/tmi.2013.2251421
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发表时间：
2013-06-01
影响因子：
10.6
通讯作者：
Purdie, Thomas G.
中科院分区：
文献类型：
--
作者：
McIntosh, Chris;Svistoun, Igor;Purdie, Thomas G.
放射疗法被用于治疗世界各地的癌症患者。高质量的治疗计划最大限度地辐射目标，同时最大限度地辐射处于危险中的健康器官。为了判断计划的质量和安全性，对有风险的目标和器官进行了分段，并在治疗之前估计了将输送到每个结构的辐射量。如果有风险的目标或器官被错误地贴上标签，或者分割质量不佳，辐射剂量的安全性将被错误地审查，并可能进行不安全的计划。为了提供质量保证和数据挖掘，我们提出了一种从放射治疗计划中自动标记不同结构的分段组的技术。在给定一个或多个分割和相关图像的情况下，我们寻求为每个分割分配具有医学意义的标签，并报告该标签的置信度。我们的方法使用随机森林来学习训练特征上的联合分布，然后利用一组学习的潜在组配置来构建条件随机场(CRF)，以确保标签的分配在分割组中是一致的。然后，通过约束分配问题来求解CRF。我们在1574张图上验证了我们的方法，包括17579个分割，总体分类准确率为91.58%。我们的结果还证明了RF对于大数据集中的树深度和分裂变量的数量的稳定性。
Radiation therapy is used to treat cancer patients around the world. High quality treatment plans maximally radiate the targets while minimally radiating healthy organs at risk. In order to judge plan quality and safety, segmentations of the targets and organs at risk are created, and the amount of radiation that will be delivered to each structure is estimated prior to treatment. If the targets or organs at risk are mislabelled, or the segmentations are of poor quality, the safety of the radiation doses will be erroneously reviewed and an unsafe plan could proceed. We propose a technique to automatically label groups of segmentations of different structures from a radiation therapy plan for the joint purposes of providing quality assurance and data mining. Given one or more segmentations and an associated image we seek to assign medically meaningful labels to each segmentation and report the confidence of that label. Our method uses random forests to learn joint distributions over the training features, and then exploits a set of learned potential group configurations to build a conditional random field (CRF) that ensures the assignment of labels is consistent across the group of segmentations. The CRF is then solved via a constrained assignment problem. We validate our method on 1574 plans, consisting of 17 579 segmentations, demonstrating an overall classification accuracy of 91.58%. Our results also demonstrate the stability of RF with respect to tree depth and the number of splitting variables in large data sets.