Migrate register_impact from impact-reg-konfai (pip + one-time glibc/LibTorch
shim) to the vboussot/elastix_impact Docker image running a rigid 6-DOF
EulerTransform with MIND + TotalSegmentator static features and a Mattes-MI
secondary metric. Delete scripts/setup_impact_konfai.sh.
Vendor ImpactLoss (vboussot/ImpactLoss) into src/impact_loss.py and add
impact_loss as a per-case semantic scoring metric in the benchmark runner.
Update docs (AGENTS.md, REGISTRATION_BENCHMARK.md) to reflect the
all-rigid pipeline set and the new Docker-based IMPACT runtime.
This commit introduces a new script, `setup_impact_konfai.sh`, which facilitates a one-time setup for the IMPACT registration pipeline (impact-reg-konfai). The script builds a self-contained shim that includes:
- A glibc 2.35 loader and libraries extracted from the niftyreg:2.1.1-ubuntu Docker image.
- A self-contained LibTorch 2.8.0 (cu128) library.
- A wrapper script that runs the stock elastix-impact binary through the glibc 2.35 loader with the appropriate library paths.
The setup ensures compatibility with hosts that do not meet the required glibc and LibTorch versions, allowing the pipeline to function correctly. The script also verifies the installation and provides usage instructions.
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.
Introduce the third segmentation pipeline using MONAI (1.6.x) to allow
direct comparison with Pipelines A and B. This includes the implementation
of the iterative pseudo-labeling workflow, training scripts, and
inference protocols.
- Add `scripts_monai/` directory containing the MONAI pipeline scripts.
- Update documentation in `README.md` and `AGENTS.md` to include MONAI
package requirements and pipeline details.
- Configure `.gitignore` to exclude MONAI-specific run directories.
- Update data directory descriptions to include MONAI pseudo-labels.
