On the degrees of freedom of gridded control points in learning‐based medical image registration
We conclude that predicting sparsely gridded displacements provides reduced computational cost and/or improved performance, independent of the encoder architecture, and can be readily implemented. Therefore, GridReg should potentially be considered for many registration tasks with adaptive grid sizes. The code is available via [email protected]:yanwenCi/GridReg.git.
Authors
- Mark Emberton
- Qianye Yang (ORCID: https://orcid.org/0000-0003-4401-5311)
- Yan Wen (ORCID: https://orcid.org/0000-0002-1282-0415)
- Yuxuan Wang (ORCID: https://orcid.org/0000-0002-1649-6974)
- Vasilis Stavrinides
- Yipeng Hu
- Shonit Punwani
- Dean Barratt
Institutions
- University College Hospital (GB)
- Imperial College Healthcare NHS Trust (GB)
- The London College (GB)
- CRUK Lung Cancer Centre of Excellence (GB)
- University College London (GB)
Publication Details
- Journal
- Medical Physics
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1002/mp.70343
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00