Title:Medical Image Processing based on Generative Adversarial Networks: A
Systematic Review
Volume: 20
Author(s): Jun Liu, Kunqi Li, Hua Dong, Yuanyuan Han and Rihui Li*
Affiliation:
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau S.A.R, China
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, CA, USA
Keywords:
Generative adversarial network, Medical image, Segmentation, Synthesis, Deep learning, Computer vision.
Abstract:
Background:
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.
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.
Results:
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.
Conclusion:
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.
