Add `register_niftyreg` as the sixth rigid CT→T1c pipeline using `reg_aladin` (symmetric block matching + trimmed least squares, NCC similarity). Runs natively via `conda install -c conda-forge niftyreg`; no Docker required. Change the benchmark's primary reported metric from geometric skull-ring QA to the achieved Mattes MI (`mi_ct_mr`, nats) between the warped CT and the original MR over the MR head. Geometric QA fields are still recorded per case in the JSON output. Raise the SimpleITK benchmark-mode optimizer budget from 150 to 2000 iterations per pyramid level so all toolkits are compared on an equal iteration footing. Update `REGISTRATION_BENCHMARK.md` (five→six pipelines, niftyreg row, elastix bin count 10→32), `AGENTS.md` (toolkit list), and `bench_registration.py` docstrings/usage accordingly. |
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| .. | ||
| 01_build_ntuh_manifest.py | ||
| 02_build_m6_dataset.py | ||
| 03_scan_lee_t1c.py | ||
| 04_reconstruct_lee.py | ||
| 05_build_splits.py | ||
| 06_pseudo_label.py | ||
| 07_train.py | ||
| 08_eval.py | ||
| 09_run_iterative.py | ||
| bench_registration.py | ||
| benchmark_pipelines.py | ||
| make_screenshots.py | ||
| preprocess.py | ||
| scan_procs.py | ||
| test_dataloader.py | ||