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首页> 外文会议>International Conference on Pattern Recognition >A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment
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A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment

机译：一种基于深入的学习方法，用于预测适应性放射治疗的鼻咽癌癌卷

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This paper presents a new system for predicting the spatial change of Nasopharyngeal carcinoma(NPC) and organ-at-risks (OARs) volumes over the course of the radiation therapy (RT) treatment for facilitating the workflow of adaptive radiotherapy. The proposed system, called “Tumor Evolution Prediction (TEP-Net)”, predicts the spatial distributions of NPC and 5 OARs, separately, in response to RT in the coming week, week n. Here, TEP-Net has (n-1)-inputs that are week 1 to week n-1 of CT axial, coronal or sagittal images acquired once the patient complete the planned RT treatment of the corresponding week. As a result, three predicted results of each target region are obtained from the three-view CT images. To determine the final prediction of NPC and 5 OARs, two integration methods, weighted fully connected layers and weighted voting methods, are introduced. From the experiments using weekly CT images of 140 NPC patients, our proposed system achieves the best performance for predicting NPC and OARs compared with conventional methods.
机译：本文提出了一种新的系统，用于预测鼻咽癌（NPC）和风险（OARS）的空间变化在放射治疗（RT）处理过程中，以促进适应放疗的工作流程的过程。所提出的系统称为“肿瘤演化预测（TEP-NET）”，预测NPC和5桨的空间分布，分别是在接下来的一周内的RT，周N.这里，一旦患者完成了一旦患者完成了相应的一周的计划RT处理，就具有（N-1）的TEP-N-1周为N-1的N-1。结果，从三维CT图像获得每个目标区域的三个预测结果。为了确定NPC和5桨的最终预测，引入了两个集成方法，加权完全连接层和加权投票方法。从使用每周CT图像的实验中的140个NPC患者，我们所提出的系统实现了与传统方法相比预测NPC和OAR的最佳性能。

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