Research progress and challenges of deep learning in medical image registration.
10.7507/1001-5515.201810004
- Author:
Maoyang ZOU
1
;
Hao YANG
2
;
Guanghui PAN
2
;
Yong ZHONG
3
Author Information
1. Chengdu University of Information Technology, Chengdu 610225, P.R.China;Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, Chengdu 610041, P.R.China.
2. Chengdu University of Information Technology, Chengdu 610225, P.R.China.
3. Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, Chengdu 610041, P.R.China.
- Publication Type:Journal Article
- Keywords:
convolutional neural networks;
deep learning;
fully convolutional networks;
medical image registration
- MeSH:
Deep Learning;
Diagnostic Imaging;
Image Processing, Computer-Assisted;
Neural Networks (Computer);
Research
- From:
Journal of Biomedical Engineering
2019;36(4):677-683
- CountryChina
- Language:Chinese
-
Abstract:
With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.
