Medical Image Processing based on Generative Adversarial Networks: A Systematic Review

计算机科学 分割 人工智能 模式 对抗制 领域(数学) 医学影像学 模态(人机交互) 生成语法 图像处理 生成对抗网络 机器学习 模式识别(心理学) 图像(数学) 数学 社会学 纯数学 社会科学
作者
Jun Liu,Kunqi Li,Hua Dong,Yuanyuan Han,Rihui Li
出处
期刊:Current Medical Imaging Reviews [Bentham Science]
卷期号:20 被引量:1
标识
DOI:10.2174/0115734056258198230920042358
摘要

Generative adversarial networks (GANs) have demonstrated superior data generation capabilities compared to other methods, making them popular for use in medical image applications. These features have intrigued researchers in the medical imaging field, resulting in a swift implementation of these techniques in various conventional and novel applications such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. A comprehensive review of recent medical imaging breakthroughs will benefit researchers interested in this field. In this review, we aimed to introduce the origin, principle, and extended forms of GANs and summarize the state-of-the-art progress of GAN-based medical image processing methods.We searched the literature for studies on Google Scholar and PubMed using the keywords "Segmentation," "Classification," "medical image," and "generative adversarial network." Specifically, the initial search revealed 5423 publications after the removal of duplicated and non-accessible fulltext publications. Then, after the title and abstract screening, 680 underwent full-text screening. Finally, 121 studies were included in our final analysis after full-text screening.The date range of the studies covered in this review is from January 1, 2017, to the present. After a thorough screening and qualification assessment, 121 studies involving GAN-based applications in seven areas of medical images were included in the final methodological review. These areas included synthesis, classification, segmentation, conversion, reconstruction, denoising, and lesion detection. We further classified and summarized these papers into clinical applications, classification methods, and imaging modalities.We thoroughly examined the latest research progress of GAN-based medical image augmentation. These techniques effectively alleviate the challenge of limited training samples for medical image diagnosis and treatment models. Furthermore, several critical issues associated with GANs, such as pattern collapse, instability, and lack of interpretability, require attention in future research.
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