fidoptuniGradICON
unigradicon-register with its defaults: the network's prediction, then 50 steps of instance optimisation on LNCC.
RegistrationGPUversion unigradicon 1.0.5, weights 1.0CT, CBCT, MRApache-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 konfai-apps infer fideus-labs/fidopt-registration:UniGradICON -i fixed.nii.gz -i moving.nii.gz -o out --gpu 0
The app on Hugging Face: its model, its settings and how it is scored.
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 | Place | Ranked | Datasets |
|---|---|---|---|
| Temporal bone CBCT, between donors | 4 | of 4 | 1 of 1 |
| Two modalities, same patient | not ranked yet | 1 of 5 | |
| Learn2Reg official validation pairs | not ranked yet | 1 of 4 |
Results
| Dataset | # | Read by | Score | Change | SDlogJ | Failed | Time s |
|---|---|---|---|---|---|---|---|
| ThoraxCBCT (Learn2Reg 2023), validation | 3 | Dice | 0.642 | +0.297 | 0.170 | 0 | 88 |
| OpenEar, inter-specimen temporal-bone CBCT | 4 | Dice | 0.161 | −0.321 | 0.350 | 0 | 132 |
- Reference
- Tian et al., MICCAI 2024