Papers › Metric Learning for Image Registration
Metric Learning for Image Registration
Marc Niethammer, Roland Kwitt, Francois-Xavier Vialard
Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Diffeomorphic Medical Image Registration | CUMC12 | Metric Net (Local Reg) | Mean target overlap ratio | 0.520 | #1 of 3 | Archive leaderboard | report |
| Diffeomorphic Medical Image Registration | CUMC12 | Metric Net (Global Reg) | Mean target overlap ratio | 0.480 | #3 of 3 | Archive leaderboard | report |
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