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
  • [41] ITU-T RECOMMENDATION P, 1999, SUBJ VID QUAL ASS ME, P34
  • [42] Jhih-Yuan Lin, 2020, Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. 23rd International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12264), P66, DOI 10.1007/978-3-030-59719-1_7
  • [43] 3D Convolutional Neural Networks for Human Action Recognition
    Ji, Shuiwang
    Xu, Wei
    Yang, Ming
    Yu, Kai
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (01) : 221 - 231
  • [44] Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution
    Jin, Xin
    Xu, Jianfeng
    Tasaka, Kazuyuki
    Chen, Zhibo
    [J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2021, 17 (01)
  • [45] Karani Neerav, 2017, Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017. 20th International Conference. Proceedings: LNCS 10434, P359, DOI 10.1007/978-3-319-66185-8_41
  • [46] An image interpolation approach for acquisition time reduction in navigator-based 4D MRI
    Karani, Neerav
    Zhang, Lin
    Tanner, Christine
    Konukoglu, Ender
    [J]. MEDICAL IMAGE ANALYSIS, 2019, 54 : 20 - 29
  • [47] Kim J, 2016, PROC CVPR IEEE, P1637, DOI [10.1109/CVPR.2016.181, 10.1109/CVPR.2016.182]
  • [48] Kim SY, 2019, IEEE IMAGE PROC, P2831, DOI [10.1109/icip.2019.8803297, 10.1109/ICIP.2019.8803297]
  • [49] Super-resolution head and neck MRA using deep machine learning
    Koktzoglou, Ioannis
    Huang, Rong
    Ankenbrandt, William J.
    Walker, Matthew T.
    Edelman, Robert R.
    [J]. MAGNETIC RESONANCE IN MEDICINE, 2021, 86 (01) : 335 - 345
  • [50] Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slice Interval
    Kudo, Akira
    Kitamura, Yoshiro
    Li, Yuanzhong
    Iizuka, Satoshi
    Simo-Serra, Edgar
    [J]. MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2019, 2019, 11905 : 91 - 100
12345678910