Deep learning in medical image super resolution: a review

被引：21
作者：
Yang, Hujun [1 ]
Wang, Zhongyang [2 ,3 ]
Liu, Xinyao [1 ]
Li, Chuangang [1 ]
Xin, Junchang [2 ,3 ]
Wang, Zhiqiong [1 ]
机构：
[1] Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang 110169, Peoples R China
[2] Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110169, Peoples R China
[3] Northeastern Univ, Key Lab Big Data Management & Analyt, Shenyang 110169, Peoples R China
来源：
APPLIED INTELLIGENCE | 2023年 / 53卷 / 18期
基金：
中国国家自然科学基金;
关键词：
Super-resolution; Medical image; Deep learning; Assessment metrics; Review; MULTI-CONTRAST SUPERRESOLUTION; QUALITY ASSESSMENT; MRI; NETWORK; SINGLE; ATTENTION; CT;
D O I：
10.1007/s10489-023-04566-9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies reconstructing corresponding high-resolution (HR) images from observed low-resolution (LR) images or image sequences. In recent years, significant breakthroughs in SR based on deep learning have been made, and many advanced results have been achieved. However, there is a lack of review literature that summarizes the field's current state and provides an outlook on future developments. Therefore, we provide a comprehensive summary of the literature on medical image SR (MedSR) based on deep learning since 2018 in five aspects: (1) The SR problem of medical images is described, and the methods of image degradation are summarized. (2) We divide the existing studies into three categories: two-dimensional image SR (2DISR), three-dimensional image SR (3DISR), and video SR (VSR). Each category is subdivided. We analyze the network structure and method characteristics of typical methods. (3) Existing SR reconstruction quality evaluation metrics are presented in detail. (4) The application of MedSR methods based on deep learning is discussed. (5) We discuss the challenges of this phase and point out valuable research directions.
引用
收藏
页码：20891 / 20916
页数：26
相关论文
共 148 条
  • [1] A review on chest CT scanning parameters implemented in COVID-19 patients: bringing low-dose CT protocols into play
    Azadbakht, Javid
    Khoramian, Daryoush
    Lajevardi, Zahra Sadat
    Elikaii, Fateme
    Aflatoonian, Amir Hossein
    Farhood, Bagher
    Najafi, Masoud
    Bagheri, Hamed
    [J]. EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE, 2021, 52 (01)
  • [2] SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
    Bai, Yancheng
    Zhang, Yongqiang
    Ding, Mingli
    Ghanem, Bernard
    [J]. COMPUTER VISION - ECCV 2018, PT XIII, 2018, 11217 : 210 - 226
  • [3] Berthelot D., 2019, INT C LEARNING REPRE, P2019
  • [4] The Perception-Distortion Tradeoff
    Blau, Yochai
    Michaeli, Tomer
    [J]. 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 6228 - 6237
  • [5] Improving mass discrimination in mammogram-CAD system using texture information and super-resolution reconstruction
    Boudraa, Sawsen
    Melouah, Ahlem
    Merouani, Hayet Farida
    [J]. EVOLVING SYSTEMS, 2020, 11 (04) : 697 - 706
  • [6] Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
    Cai, Jie
    Meng, Zibo
    Ho, Chiu Man
    [J]. 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020), 2020, : 1852 - 1861
  • [7] GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
    Chan, Kelvin C. K.
    Wang, Xintao
    Xu, Xiangyu
    Gu, Jinwei
    Loy, Chen Change
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 14240 - 14249
  • [8] BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
    Chan, Kelvin C. K.
    Wang, Xintao
    Yu, Ke
    Dong, Chao
    Loy, Chen Change
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 4945 - 4954
  • [9] Learning Spatial Attention for Face Super-Resolution
    Chen, Chaofeng
    Gong, Dihong
    Wang, Hao
    Li, Zhifeng
    Wong, Kwan-Yee K.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 1219 - 1231
  • [10] Double paths network with residual information distillation for improving lung CT image super resolution
    Chen, Yihan
    Zheng, Qianying
    Chen, Jiansen
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 73
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