fidoptcorrField
corrfield.py with its defaults, on the authors' 1 mm grid or coarser: Foerstner keypoints inside the fixed image's lung mask, MIND-SSC, discrete matching, thin-plate splines.
RegistrationGPUversion 53598calungApache-2.0
Run it
With its KonfAI app, at the benchmark's settings (the fidopt app repositories are private for now: a Hugging Face token with access opens them):
pip install konfai-apps
| configuration | Command | |
|---|---|---|
| corrField | Get the app | konfai-apps infer fideus-labs/fidopt-registration:CorrField -i fixed.nii.gz -i moving.nii.gz -o out --gpu 0 |
| Learn2Reg organisers' baseline | Get the app | konfai-apps infer fideus-labs/fidopt-registration:CorrField -i fixed.nii.gz -i moving.nii.gz -o out --gpu 0 --set=code=l2r |
Where it works
Its place on each kind of problem it has results on, among the methods ranked there. A method that leads one problem can trail another: read the one closest to yours.
| Problem | configuration | Place | Ranked | Datasets |
|---|---|---|---|---|
| Two modalities, same patient | not ranked yet | 1 of 5 | ||
| Learn2Reg official validation pairs | not ranked yet | 1 of 4 |
Results
| Dataset | configuration | # | Read by | Score | Change | SDlogJ | Failed | Time s |
|---|---|---|---|---|---|---|---|---|
| ThoraxCBCT (Learn2Reg 2023), validation | 4 | Dice | 0.589 | +0.244 | 0.801 | 0 | 62 | |
| LUNA25 | 1 | TRE mm | 0.71 | −6.37 | 0.019 | 0 | 32 | |
| Lung-DIR-QA | 1 | TRE mm | 0.84 | −12.14 | 0.043 | 0 | 54 |
- Reference
- Heinrich et al., MICCAI 2015