Deep learning methods for medical image fusion: A review
文献类型:期刊论文
作者 | Zhou, Tao2,5; Cheng, QianRu2,5; Lu, HuiLing1,4; Li, Qi2,5; Zhang, XiangXiang2,5; Qiu, Shi3 |
刊名 | COMPUTERS IN BIOLOGY AND MEDICINE |
出版日期 | 2023-06 |
卷号 | 160 |
ISSN号 | 0010-4825;1879-0534 |
关键词 | Deep learning Medical image fusion Convolutional neural network Generative adversarial network Encoder -decoder network |
DOI | 10.1016/j.compbiomed.2023.106959 |
产权排序 | 3 |
英文摘要 | The image fusion methods based on deep learning has become a research hotspot in the field of computer vision in recent years. This paper reviews these methods from five aspects: Firstly, the principle and advantages of image fusion methods based on deep learning are expounded; Secondly, the image fusion methods are summarized in two aspects: End-to-End and Non-End-to-End, according to the different tasks of deep learning in the feature processing stage, the non-end-to-end image fusion methods are divided into two categories: deep learning for decision mapping and deep learning for feature extraction. According to the different types of the networks, the end-to-end image fusion methods are divided into three categories: image fusion methods based on Convolutional Neural Network, Generative Adversarial Network, and Encoder-Decoder Network; Thirdly, the application of the image fusion methods based on deep learning in medical image field is summarized from two aspects: method and data set; Fourthly, evaluation metrics commonly used in the field of medical image fusion are sorted out from 14 aspects; Fifthly, the main challenges faced by the medical image fusion are discussed from two aspects: data sets and fusion methods. And the future development direction is prospected. This paper systematically summarizes the image fusion methods based on the deep learning, which has a positive guiding significance for the in-depth study of multi modal medical images. |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:000992823400001 |
源URL | [http://ir.opt.ac.cn/handle/181661/96483] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Cheng, QianRu; Lu, HuiLing |
作者单位 | 1.Ningxia Med Univ, Sch Sci, Yinchuan, Peoples R China 2.North Minzu Univ, Key Lab Image & Intelligent Proc, State Ethn Affairs Commiss, Yinchuan 750021, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China 4.Ningxia Med Univ, Sch Sci, Yinchuan 750004, Peoples R China 5.North Minzu Univ, Sch Comp Sci & Engn, Yinchuan 750021, Peoples R China |
推荐引用方式 GB/T 7714 | Zhou, Tao,Cheng, QianRu,Lu, HuiLing,et al. Deep learning methods for medical image fusion: A review[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2023,160. |
APA | Zhou, Tao,Cheng, QianRu,Lu, HuiLing,Li, Qi,Zhang, XiangXiang,&Qiu, Shi.(2023).Deep learning methods for medical image fusion: A review.COMPUTERS IN BIOLOGY AND MEDICINE,160. |
MLA | Zhou, Tao,et al."Deep learning methods for medical image fusion: A review".COMPUTERS IN BIOLOGY AND MEDICINE 160(2023). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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