review-article

Generative adversarial networks in medical image segmentation: : A review

Authors:

Pu HuangAuthors Info & Claims

Volume 140, Issue C

https://doi.org/10.1016/j.compbiomed.2021.105063

Published: 01 January 2022 Publication History

Abstract

Purpose

Since Generative Adversarial Network (GAN) was introduced into the field of deep learning in 2014, it has received extensive attention from academia and industry, and a lot of high-quality papers have been published. GAN effectively improves the accuracy of medical image segmentation because of its good generating ability and capability to capture data distribution. This paper introduces the origin, working principle, and extended variant of GAN, and it reviews the latest development of GAN-based medical image segmentation methods.

Method

To find the papers, we searched on Google Scholar and PubMed with the keywords like “segmentation”, “medical image”, and “GAN (or generative adversarial network)”. Also, additional searches were performed on Semantic Scholar, Springer, arXiv, and the top conferences in computer science with the above keywords related to GAN.

Results

We reviewed more than 120 GAN-based architectures for medical image segmentation that were published before September 2021. We categorized and summarized these papers according to the segmentation regions, imaging modality, and classification methods. Besides, we discussed the advantages, challenges, and future research directions of GAN in medical image segmentation.

Conclusions

We discussed in detail the recent papers on medical image segmentation using GAN. The application of GAN and its extended variants has effectively improved the accuracy of medical image segmentation. Obtaining the recognition of clinicians and patients and overcoming the instability, low repeatability, and uninterpretability of GAN will be an important research direction in the future.

Graphical abstract

Display Omitted

Highlights

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A systematic review on medical image segmentation based on GAN is provided.

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The origin, working principle and extended variant of GAN are introduced.

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More than 120 papers are categorized and summarized in different ways.

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The challenge and future research of medical image segmentation using GAN are discussed.

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Wang XRen XJin GYing SWang JLi JShi J(2024)B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completionComputers in Biology and Medicine10.1016/j.compbiomed.2024.109106182:COnline publication date: 1-Nov-2024
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Sun HWei JYuan WLi R(2024)Semi-supervised multi-modal medical image segmentation with unified translationComputers in Biology and Medicine10.1016/j.compbiomed.2024.108570176:COnline publication date: 18-Jul-2024
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Index Terms

Generative adversarial networks in medical image segmentation: A review
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 140, Issue C

Jan 2022

672 pages

ISSN:0010-4825

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Pergamon Press, Inc.

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Published: 01 January 2022

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Wang XRen XJin GYing SWang JLi JShi J(2024)B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completionComputers in Biology and Medicine10.1016/j.compbiomed.2024.109106182:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.109106
Sun HWei JYuan WLi R(2024)Semi-supervised multi-modal medical image segmentation with unified translationComputers in Biology and Medicine10.1016/j.compbiomed.2024.108570176:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108570
Yu XQin YZhang FZhang Z(2024)A recurrent positional encoding circular attention mechanism network for biomedical image segmentationComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108054246:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108054
Biswas AMaity SBanik RBhattacharya PDebbarma J(2024)GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical ImagingSN Computer Science10.1007/s42979-024-02991-25:5Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1007/s42979-024-02991-2
He ZJia DZhang CLi ZWu N(2024)A two-stage approach solo_GAN for overlapping cervical cell segmentation based on single-cell identification and boundary generationApplied Intelligence10.1007/s10489-024-05378-154:6(4621-4645)Online publication date: 1-Mar-2024
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Chen CChen YSong SWang JNing HXiao REl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Cerebrovascular Segmentation in TOF-MRA with Topology Regularization Adversarial ModelProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611718(4250-4259)Online publication date: 26-Oct-2023
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Xu BFan YLiu JZhang GWang ZLi ZGuo WTang X(2023)CHSNetComputers in Biology and Medicine10.1016/j.compbiomed.2023.107334164:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107334
Makhlouf AMaayah MAbughanam NCatal C(2023)The use of generative adversarial networks in medical image augmentationNeural Computing and Applications10.1007/s00521-023-09100-z35:34(24055-24068)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00521-023-09100-z
Bogensperger LNarnhofer DIlic FPock T(2023)Score-Based Generative Models for Medical Image Segmentation Using Signed Distance FunctionsPattern Recognition10.1007/978-3-031-54605-1_1(3-17)Online publication date: 19-Sep-2023
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Benfenati ACatozzi AFranchini GPorta F(2023)Piece-wise Constant Image Segmentation with a Deep Image Prior ApproachScale Space and Variational Methods in Computer Vision10.1007/978-3-031-31975-4_27(352-362)Online publication date: 21-May-2023
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Siyi Xun

Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China

Dengwang Li

Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China

Hui Zhu

Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China

Min Chen

The Second Hospital of Shandong University, Shandong University, The Department of Medicine, The Second Hospital of Shandong University, Jinan, China

Jianbo Wang

Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China

Jie Li

Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China

Meirong Chen

Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China

Bing Wu

Laibo Biotechnology Co., Ltd., Jinan, Shandong, China

Hua Zhang

LinkingMed Technology Co., Ltd., Beijing, China

Xiangfei Chai

Huiying Medical Technology (Beijing) Co., Ltd., Beijing, China

Zekun Jiang

Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China

Yan Zhang

Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China

Pu Huang

Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China

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