Multiscale unsupervised network for deformable image registration
Our proposed FMIRNet is effective for large deformation estimation, and its registration capability is generalizable and robust in joint registration and segmentation frameworks to generate reliable labels for training segmentation tasks.
Authors
- Wanru Chang (ORCID: https://orcid.org/0000-0003-1078-1894)
- Dexing Kong (ORCID: https://orcid.org/0000-0001-9339-8086)
- Chongfei Huang (ORCID: https://orcid.org/0009-0008-4725-7854)
- Yun Wang (ORCID: https://orcid.org/0000-0001-8619-0455)
Institutions
- Zhejiang A & F University (CN)
- China Mobile (China) (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Journal of X-Ray Science and Technology
- Published
- 2024-09-04
- DOI
- https://doi.org/10.3233/xst-240159
- Citations
- 1
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.25