Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces
Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/129526 |