| 基于深度学习的医学图像配准技术研究进展 |
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| 引用本文： | 郭艳芬,崔喆,杨智鹏,彭静,胡金蓉. 基于深度学习的医学图像配准技术研究进展[J]. 计算机工程与应用, 2021, 57(15): 1-8. DOI: 10.3778/j.issn.1002-8331.2101-0281 |
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| 作者姓名： | 郭艳芬 崔喆 杨智鹏 彭静 胡金蓉 |
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| 作者单位： | 1.中国科学院 成都计算机应用研究所,成都 610041 2.中国科学院大学,北京 1000493.成都信息工程大学 计算机学院,成都 610225 |
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| 摘 要： | 医学图像配准技术对于病灶检测、临床诊断、手术规划,疗效评估等有着广泛的应用价值.系统性地总结了基于深度学习的配准算法,从深度迭代、全监督、弱监督到无监督学习的研究发展趋势,分析了各种方法的优势与局限.总体来看,无论是对数据的要求、配准精度,还是计算效率,无监督学习因其不依赖金标准和解剖标签,采用端到端的网络配准框架就可...
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| 关 键 词： | 医学图像配准 深度学习 无监督学习 多模态医学图像 |
Research Progress of Medical Image Registration Technology Based on Deep Learning |
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| GUO Yanfen,CUI Zhe,YANG Zhipeng,PENG Jing,HU Jinrong. Research Progress of Medical Image Registration Technology Based on Deep Learning[J]. Computer Engineering and Applications, 2021, 57(15): 1-8. DOI: 10.3778/j.issn.1002-8331.2101-0281 |
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| Authors: | GUO Yanfen CUI Zhe YANG Zhipeng PENG Jing HU Jinrong |
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| Affiliation: | 1.Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China 2.University of Chinese Academy of Sciences, Beijing 100049, China3.School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China |
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| Abstract: | Medical image registration technology has a wide range of application values for lesion detection, clinical diagnosis, surgical planning, and efficacy evaluation. This paper systematically summarizes the registration algorithm based on deep learning, and analyzes the advantages and limitations of various methods from deep iteration, full supervision, weak supervision to unsupervised learning. In general, unsupervised learning has become the mainstream direction of medical image registration research, because it does not rely on golden standards and uses an end-to-end network to save time. Meanwhile, compared with other methods, unsupervised learning can achieve higher accuracy and spends shorter time. However, medical image registration methods based on unsupervised learning also face some research difficulties and challenges in terms of interpretability, cross-modal diversity, and repeatable scalability in the field of medical images, which points out the research direction for achieving more accurate medical image registration methods in the future. |
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| Keywords: | medical image registration deep learning unsupervised learning multi-modal medical image |
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