RegistrationBias-sCT: checkpoints, registrations and simulated CBCT
Model weights, registration files and simulated CBCT of “When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation” (arXiv:2609.29387).
The code, the configurations, the patient-level results and the step-by-step reproduction guide are on GitHub: vboussot/RegistrationBias-sCT.
Content
checkpoints/Task_{1,2}/{ELX,IMPACT}/{MAE,SAM,VGG}/CV_{0..4}.pt 50 checkpoints, 15.7 GB
checkpoints/manifest.csv size and SHA-256 of each checkpoint
transforms/synthrad2025-elx-registration/Task_{1,2}/{AB,HN,TH}/ 169 Elastix transforms, 0.2 GB
sim-cbct/{AB,HN,TH}/{case}.mha 103 simulated CBCT, 0.5 GB
Checkpoints
Synthetic CT generators: a 2.5D U-Net++ with a ResNet-34 encoder (five adjacent axial slices, 26 M parameters), trained with KonfAI on the SynthRAD2025 abdomen, head-and-neck and thorax cases.
| Path element | Values | Meaning |
|---|---|---|
Task_1, Task_2 |
MR-to-CT, CBCT-to-CT | |
ELX, IMPACT |
registration used to build the training pairs: organizers' Elastix, or IMPACT-Reg | |
MAE, SAM, VGG |
training loss: MAE alone, MAE + SAM 2.1 features, MAE + VGG (IMPACT pairs only) | |
CV_0 … CV_4 |
cross-validation fold; each file is the checkpoint with the lowest validation MAE of its fold |
That is 2 tasks × (ELX: MAE, SAM; IMPACT: MAE, SAM, VGG) × 5 folds = 50 files, about 313 MB each. A prediction of the paper averages the five folds of a model, each applied to the image and two flipped copies.
ELX transforms
Elastix B-spline transform parameter files of the 169 held-out SynthRAD2025
cases (Task 1: 66, Task 2: 103), one {case}.txt per case. They register the
planning CT to the MR or CBCT and are computed with the organizers' parameter
files
(SynthRAD2025/preprocessing,
commit 8a5b125).
The IMPACT-Reg transforms are in VBoussot/synthrad2023-impact-registration and VBoussot/synthrad2025-impact-registration.
Simulated CBCT
The Sim-CBCT test set of the paper: one CBCT simulated from the planning CT of
each of the 103 held-out SynthRAD2025 Task 2 cases (abdomen 32, head and neck
37, thorax 34) by RTK forward projection, noise, scatter and FDK reconstruction
(scripts/preprocessing/cbct_synthesis.py in the GitHub repository). Each
volume is on the grid of its CT, in HU, and set to -1024 outside the patient
mask. The CT and the masks are in the
SynthRAD2025 training set.
License
The checkpoints and the ELX transforms are released under Apache-2.0. The simulated CBCT derive from SynthRAD2025 images and are released under their license, CC BY-NC 4.0.
Use
The GitHub repository downloads these files to the right place and checks the SHA-256 of each checkpoint:
git clone https://github.com/vboussot/RegistrationBias-sCT && cd RegistrationBias-sCT
python scripts/download.py --only checkpoints elx sim-cbct
python scripts/predict.py --gpu 0 # see docs/REPRODUCE.md
One model alone:
hf download VBoussot/RegistrationBias-sCT --include "checkpoints/Task_1/IMPACT/MAE/*" --local-dir .
Citation
@article{boussot2026misalignment,
title = {When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation},
author = {Boussot, Valentin and H{\'e}mon, C{\'e}dric and Lafond, Caroline and Nunes, Jean-Claude and Dillenseger, Jean-Louis},
journal = {arXiv preprint arXiv:2609.29387},
year = {2026}
}