A deep learning-based algorithm for three-dimensional dose prediction

Mengjia Xue, Tianrui Liu, Yizhen Xie, Meiya Dong, Xiuping Liu
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引用次数: 0

Abstract

Three-dimensional dose prediction is an important step in automatic radiotherapy planning. Using deep learning combined with Knowledge-Based Planning methods (KBP) can achieve dose distribution prediction on CT images. Convolutional Neural Networks (CNN) are an important branch of deep learning algorithms. This article will briefly introduce the application of convolutional neural networks and other advanced algorithm structures in dose prediction. And there are different studies that evaluate the model output results by studying different transformation models, different patient data and data of different treatment methods, and find the optimal dose prediction model. However, the research on dose prediction models is not the most complete. There is still room for further research in terms of input tumor types, treatment methods, etc. Moreover, automatic radiotherapy plan generation is the ultimate goal, and further research is needed.
基于深度学习的三维剂量预测算法
三维剂量预测是放射治疗自动规划的重要步骤。将深度学习与知识规划方法(Knowledge-Based Planning methods, KBP)相结合,可以实现CT图像的剂量分布预测。卷积神经网络(CNN)是深度学习算法的一个重要分支。本文将简要介绍卷积神经网络和其他先进的算法结构在剂量预测中的应用。也有不同的研究通过研究不同的转化模型、不同的患者数据和不同治疗方法的数据来评价模型输出结果,找到最优的剂量预测模型。然而,剂量预测模型的研究还不是最完整的。在输入肿瘤类型、治疗方法等方面仍有进一步研究的空间。而放疗计划的自动生成是最终的目标,需要进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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