End‐to‐end unsupervised cycle‐consistent fully convolutional network for 3D pelvic CT‐MR deformable registration-Reference-Cited by-同舟云学术

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End‐to‐end unsupervised cycle‐consistent fully convolutional network for 3D pelvic CT‐MR deformable registration

Published:2020-07-13 Issue:9 Volume:21 Page:193-200
ISSN:1526-9914
Container-title:Journal of Applied Clinical Medical Physics
language:en
Short-container-title:J. Appl. Clin. Med. Phys.

Author:

Guo Yi¹,Wu Xiangyi¹^ORCID,Wang Zhi¹²,Pei Xi¹,Xu X. George¹³

Affiliation:

1. Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui China

2. Department of Radiology The First Affiliated Hospital of Anhui Medical University of China Hefei Anhui China

3. Nuclear Engineering Program Rensselaer Polytechnic Institute Troy NY USA

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.12968

Reference30 articles.

1. Deformable Medical Image Registration: A Survey

2. SloanJM GoatmanKA SiebertJP.Learning Rigid Image Registration‐Utilizing Convolutional Neural Networks for Medical Image Registration. In:11th International Joint Conference on Biomedical Engineering Systems and Technologies. Funchal Madeira Portugal: SCITEPRESS ‐ Science and Technology Publications;2018:89–99.

3. A survey of medical image registration – under review

4. Medical image registration: a review

5. MahapatraD.Elastic registration of medical images with gans. arXiv preprint arXiv:1805.02369;2018.https://arxiv.org/pdf/1805.02369.pdf.

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