Validation and Benchmarking
Validation and performance are separate acceptance axes. A faster run is not an accuracy result, and a lower metric is not proof of correct geometry.
Registration validation should combine:
- shape, spacing, origin, and direction checks;
- metric values before and after;
- overlap measures when labels exist;
- convergence state and iteration budget; and
- a labeled overlay plus an input-to-output change map.
Benchmarking should measure loading, preprocessing, metric evaluation, resampling, and optimizer loops separately. This makes a regression attributable to an algorithm rather than a hidden boundary copy.
Example Summary
| Example | Status | Focus |
|---|---|---|
| Geometry Validation | Available | Baseline spatial-contract check before trusting a registration result. |
| Validation Suite | Available | Aggregate geometry, metric, overlap, and convergence checks. |
| Gradient Recursive Gaussian Benchmark | Available | Performance methodology for reusable filter kernels. |
The CT/MR example records both the identity-to-registered metric change and the visible resampling change so those claims remain distinguishable.