Abstract
We propose a data-driven approach for deformable image registration that extends the TransMorph architecture with a module based on the flow equation formulated on matrix fields. Choosing a suitable Lie group allows us to precisely control components of the deformation that should be ignored by the regularization and provides a powerful plug-in replacement for the deformation field generator in learning-based methods. Combined with the TransMorph architecture, our method performs favorably on the IXI and OASIS datasets. Our implementation is available at https://github.com/sennhoj321/Liemorph.
| Original language | English |
|---|---|
| Number of pages | 12 |
| Publication status | Published - 2025 |
| Event | The 36th British Machine Vision Conference (BMVC) - Cutler's Hall, Sheffield, Sheffield, United Kingdom Duration: 24.11.2025 → 27.11.2025 https://bmvc2025.bmva.org |
Conference
| Conference | The 36th British Machine Vision Conference (BMVC) |
|---|---|
| Abbreviated title | BMVC 2025 |
| Country/Territory | United Kingdom |
| City | Sheffield |
| Period | 24.11.25 → 27.11.25 |
| Internet address |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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