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Demons

ITK's fast symmetric forces Demons as SimpleITK's Demons notebook runs it: 20 iterations, field smoothing 2, pyramid 4, 2, 1; on MR the moving histogram is first matched to the fixed one, as SimpleITK's Demons examples do.

RegistrationCPUversion SimpleITK 2.5.6, notebook 190ad08, examples 9b49b9cCT, 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:Demons -i fixed.nii.gz -i moving.nii.gz -o out

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.

Results

Dataset#Read byScoreChangeSDlogJFailedTime s
ACDC3Dice0.731+0.1730.03501
Pediatric T1 MRI with and without previous ADHD diagnosis3Dice0.485+0.0730.05409
AFIDs1Dice0.477+0.1070.072015
AMOS CT/CT1Dice0.293+0.1130.964032
AMOS MR/MR2Dice0.358+0.0930.112014
Abdominal-DIR-QA1Dice0.712+0.2810.088044
Baby Open Brains (BOBs)2Dice0.719+0.1230.05106
BraTS-Reg1TRE mm4.79−1.540.02607
Breast-MRI-NACT-Pilot2Dice0.642+0.1330.09805
Colorectal-Liver-Metastases, liver and veins1Dice0.310+0.0940.235018
CC-Tumor-Heterogeneity1TRE mm7.17−1.980.13804
Ex vivo medial temporal lobe MRI (Penn)1Dice0.393+0.0670.061046
Weekly ADC on the MR-Linac, head and neck1TRE mm6.57+2.290.05303
HNTS-MRG 20242TRE mm3.93−0.280.076019
HQColon1Dice0.695+0.1780.0740149
PIANO hand MRI1TRE mm33.53−1.650.106017
Human Inner Ear Anatomy1Dice0.411+0.0150.058087
OAIZIB-CM2Dice0.511+0.1060.090019
LCTSC2Dice0.691+0.2390.111032
LUMIR (Learn2Reg 2024), training images2Dice0.594+0.1330.02606
LUNA253TRE mm2.83−4.250.077037
Liver-DIR-QA2Dice0.658+0.2190.100035
CT follow-up of liver metastases2Dice0.846+0.1120.066010
PLC-CECT2Dice0.948+0.0420.033032
Lung-DIR-QA2TRE mm9.79−3.200.061064
M-CRIB 2.0 and M-CRIB-WM2Dice0.473+0.1140.046013
MSLesSeg1Dice0.727−0.0730.034012
Mindboggle-1011Dice0.407+0.1090.02609
MMB-MRI2Dice0.626−0.0980.02302
PDDCA2Dice0.344+0.1430.120031
PROMISE125Dice0.627+0.0110.54205
PSMA-PET-CT-Lesions follow-up (Learn2Reg 2026 PSMAReg)4TRE mm5.50−2.080.082059
Pancreas-CT, 8 organs1Dice0.368+0.0850.084053
Pediatric thoracic dynamic MRI1Dice0.898+0.0540.03002
Pelvic landmarks CT/CT (LUMC)5Dice0.728+0.1120.087031
QIN-PROSTATE-Repeatability5Dice0.519−0.1251.18007
QUADRA_HC, whole-body CT test-retest5Dice0.811+0.2590.052045
RIDER Lung CT3Dice0.781+0.0450.054052
RibSeg v21Dice0.142+0.0990.088081
Spine-generic multi-subject3Dice0.632+0.3020.06706
Tongue musculature MRI2Dice0.553+0.0550.021010
TotalSegmentator MRI3Dice0.271+0.0870.10906
TrackRAD2025, two frames of a sagittal cine MR sequence1Dice0.773+0.2380.05100
UCSD-BMETS-Longitudinal2Dice0.690−0.1010.022061
UltraCortex4Dice0.555+0.0580.051036
Source
github.com/InsightSoftwareConsortium/SimpleITK-Notebooks/blob/190ad08ccd84164306a8f574df2d4e7cb1724b3f/Python/66_Registration_Demons.ipynb
Reference
Thirion, Medical Image Analysis 1998