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 50 条
  • [41] A review on deep learning in medical image analysis
    Suganyadevi, S.
    Seethalakshmi, V
    Balasamy, K.
    INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL, 2022, 11 (01) : 19 - 38
  • [42] A review on deep learning in medical image analysis
    S. Suganyadevi
    V. Seethalakshmi
    K. Balasamy
    International Journal of Multimedia Information Retrieval, 2022, 11 : 19 - 38
  • [43] A Review on Deep Learning in Medical Image Reconstruction
    Zhang, Hai-Miao
    Dong, Bin
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA, 2020, 8 (02) : 311 - 340
  • [44] A Review of Deep Learning on Medical Image Analysis
    Jian Wang
    Hengde Zhu
    Shui-Hua Wang
    Yu-Dong Zhang
    Mobile Networks and Applications, 2021, 26 : 351 - 380
  • [45] A Review of Deep Learning on Medical Image Analysis
    Wang, Jian
    Zhu, Hengde
    Wang, Shui-Hua
    Zhang, Yu-Dong
    Mobile Networks and Applications, 2021, 26 (01) : 351 - 380
  • [46] Deep learning in medical image registration: a review
    Fu, Yabo
    Lei, Yang
    Wang, Tonghe
    Curran, Walter J.
    Liu, Tian
    Yang, Xiaofeng
    PHYSICS IN MEDICINE AND BIOLOGY, 2020, 65 (20):
  • [47] A Review of Deep Learning on Medical Image Analysis
    Wang, Jian
    Zhu, Hengde
    Wang, Shui-Hua
    Zhang, Yu-Dong
    MOBILE NETWORKS & APPLICATIONS, 2021, 26 (01): : 351 - 380
  • [48] A Review on Deep Learning in Medical Image Reconstruction
    Hai-Miao Zhang
    Bin Dong
    Journal of the Operations Research Society of China, 2020, 8 : 311 - 340
  • [49] Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising
    Li, Jintao
    Wu, Xinming
    Hu, Zhanxuan
    IEEE Transactions on Geoscience and Remote Sensing, 2022, 60
  • [50] Learning a Mixture of Deep Networks for Single Image Super-Resolution
    Liu, Ding
    Wang, Zhaowen
    Nasrabadi, Nasser
    Huang, Thomas
    COMPUTER VISION - ACCV 2016, PT III, 2017, 10113 : 145 - 156
12345