Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions-Reference-Cited by-同舟云学术

Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions

Published:2023 Issue: Volume:32 Page:4880-4892
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Fan Xin¹^ORCID,Li Zi¹^ORCID,Li Ziyang¹,Wang Xiaolin¹,Liu Risheng¹^ORCID,Luo Zhongxuan¹,Huang Hao²^ORCID

Affiliation:

1. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province and the DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

2. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Funder

National Natural Science Foundation of China

Liaoning Revitalization Talents Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9991910/10231109.pdf?arnumber=10231109

Reference50 articles.

1. Bi-level Probabilistic Feature Learning for Deformable Image Registration

2. DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration

3. Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks

4. OCTRexpert: A Feature-Based 3D Registration Method for Retinal OCT Images

5. Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces

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