Narrative review of generative adversarial networks in medical and molecular imaging.
Narrative review of generative adversarial networks in medical and molecular imaging.
复制标题
医学和分子成像中生成对抗网络的叙述回顾。
DOI：
10.21037/atm-20-6325
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发表时间：
2021-05
影响因子：
--
通讯作者：
Rowe SP
中科院分区：
文献类型：
--
作者：
Koshino K;Werner RA;Pomper MG;Bundschuh RA;Toriumi F;Higuchi T;Rowe SP
近年来，人工智能和机器学习在医学成像领域的应用迅速扩大。生成对抗网络（GAN）是基于人工神经网络和深度学习的图像合成技术。除了 GAN 所基于的深度学习固有的灵活性和多功能性之外，GAN 潜在的解决问题的能力也引起了人们的关注，并在医学和分子成像领域得到了大力研究。这篇叙述性评论对 GAN 进行了全面的概述，并讨论了它们在医学和分子成像中的以下主题的有用性：（I）数据增强，以增加基于人工智能的计算机辅助诊断的训练数据，作为此类训练集数据匮乏性质的解决方案； (II) 模态转换，以补充反映某些物理测量原理的单一模态的缺点，例如从磁共振（MR）图像到计算机断层扫描（CT）图像，反之亦然； （三）去噪，实现核医学、CT更少的注射和/或辐射剂量； (IV)图像重建，以缩短MR采集时间，同时保持高图像质量； (V) 超分辨率，从低分辨率图像产生高分辨率图像； (VI)领域适应，利用监督标签和注释等知识从源领域到没有知识或知识不足的目标领域； (VII) 具有疾病严重程度和放射基因组学的图像生成。 GAN 是很有前景的医学和分子成像工具。模型架构及其应用的进展应该继续值得注意。
Recent years have witnessed a rapidly expanding use of artificial intelligence and machine learning in medical imaging. Generative adversarial networks (GANs) are techniques to synthesize images based on artificial neural networks and deep learning. In addition to the flexibility and versatility inherent in deep learning on which the GANs are based, the potential problem-solving ability of the GANs has attracted attention and is being vigorously studied in the medical and molecular imaging fields. Here this narrative review provides a comprehensive overview for GANs and discuss their usefulness in medical and molecular imaging on the following topics: (I) data augmentation to increase training data for AI-based computer-aided diagnosis as a solution for the data-hungry nature of such training sets; (II) modality conversion to complement the shortcomings of a single modality that reflects certain physical measurement principles, such as from magnetic resonance (MR) to computed tomography (CT) images or vice versa; (III) de-noising to realize less injection and/or radiation dose for nuclear medicine and CT; (IV) image reconstruction for shortening MR acquisition time while maintaining high image quality; (V) super-resolution to produce a high-resolution image from low-resolution one; (VI) domain adaptation which utilizes knowledge such as supervised labels and annotations from a source domain to the target domain with no or insufficient knowledge; and (VII) image generation with disease severity and radiogenomics. GANs are promising tools for medical and molecular imaging. The progress of model architectures and their applications should continue to be noteworthy.
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影响因子：
1.9
作者：
Kazuhiro, Koshino;Werner, Rudolf A.;Rowe, Steven P.
通讯作者：
Rowe, Steven P.
影响因子：
10.6
作者：
Mardani M;Gong E;Cheng JY;Vasanawala SS;Zaharchuk G;Xing L;Pauly JM
通讯作者：
Pauly JM
DOI：
10.1016/j.compmedimag.2019.101684
发表时间：
2020-01-01
影响因子：
5.7
作者：
Armanious, Karim;Jiang, Chenming;Yang, Bin
通讯作者：
Yang, Bin
影响因子：
5.5
作者：
Chartrand, Gabriel;Cheng, Phillip M.;Tang, An
通讯作者：
Tang, An
影响因子：
1.5
作者：
Armanious, Karim;Kuestner, Thomas;Gatidis, Sergios
通讯作者：
Gatidis, Sergios