工作流程
放射治疗
计算机科学
人工智能
自动化
医学物理学
医学
放射科
工程类
数据库
机械工程
作者
Yabo Fu,Hao Zhang,Eric D. Morris,Carri Glide‐Hurst,Suraj Pai,Alberto Traverso,Leonard Wee,Ibrahim Hadžić,Per-Ivar Lønne,Chenyang Shen,Tian Liu,Xiaofeng Yang
出处
期刊:IEEE transactions on radiation and plasma medical sciences
[Institute of Electrical and Electronics Engineers]
日期:2021-08-24
卷期号:6 (2): 158-181
被引量:30
标识
DOI:10.1109/trpms.2021.3107454
摘要
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
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