Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19042 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915757495943168 |
|---|---|
| author | Vati, Ines Bourgeat, Pierrick Cruz, Rodrigo Santa Dore, Vincent Salvado, Olivier Fookes, Clinton Lebrat, Léo |
| author_facet | Vati, Ines Bourgeat, Pierrick Cruz, Rodrigo Santa Dore, Vincent Salvado, Olivier Fookes, Clinton Lebrat, Léo |
| contents | We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and meshes. It can learn from meshes of arbitrary size and provide learnt features at any resolution. Neural cortical maps enable efficient optimization on the sphere and achieve runtimes up to 30 times faster than classic barycentric interpolation (for the same number of iterations). As a proof of concept, we investigate rigid registration of cortical surfaces and propose NC-Reg, a novel iterative algorithm that involves the use of neural cortical feature maps, gradient descent optimization and a simulated annealing strategy. Through ablation studies and subject-to-template experiments, our method demonstrates sub-degree accuracy ($<1^\circ$ from the global optimum), and serves as a promising robust pre-alignment strategy, which is critical in clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19042 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NC-Reg : Neural Cortical Maps for Rigid Registration Vati, Ines Bourgeat, Pierrick Cruz, Rodrigo Santa Dore, Vincent Salvado, Olivier Fookes, Clinton Lebrat, Léo Computer Vision and Pattern Recognition We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and meshes. It can learn from meshes of arbitrary size and provide learnt features at any resolution. Neural cortical maps enable efficient optimization on the sphere and achieve runtimes up to 30 times faster than classic barycentric interpolation (for the same number of iterations). As a proof of concept, we investigate rigid registration of cortical surfaces and propose NC-Reg, a novel iterative algorithm that involves the use of neural cortical feature maps, gradient descent optimization and a simulated annealing strategy. Through ablation studies and subject-to-template experiments, our method demonstrates sub-degree accuracy ($<1^\circ$ from the global optimum), and serves as a promising robust pre-alignment strategy, which is critical in clinical settings. |
| title | NC-Reg : Neural Cortical Maps for Rigid Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.19042 |