Champollion V1

Self-supervised SSL models for generating embeddings of cortical folding patterns from T1 MRI brain scans.

This repository is used as a git submodule inside champollion_pipeline.

Pre-trained models are published on Hugging Face: neurospin/Champollion_V1.

What it does

Given preprocessed 3D brain crops of sulcal regions (ROIs), each model fold produces a fixed-size embedding vector per subject. The repository covers 56 sulcal regions (28 regions × 2 hemispheres), matching the regions defined in champollion_pipeline/sulci_regions_champollion_V1.json.

• Architecture: SSL (Barlow Twins) with a CNN backbone
• Input: 3D numpy crops of sulcal regions
• Output: Fixed-size embedding vectors (one per subject per region)
• Training data: UKBioBank (42,433 subjects)

Installation

This submodule is installed automatically by champollion_pipeline:

git clone https://github.com/neurospin/champollion_pipeline.git
cd champollion_pipeline
pixi run install-all

It can also be installed as a standalone deep learning framework :

git clone https://github.com/neurospin/champollion.git
cd champollion
virtualenv --python=python3 --system-site-packages venv
. venv/bin/activate
pip install -e .

Mask versions

Models are organised by ROI mask version inside the Hugging Face repo:

Version	Description
canonical_25	Original mask version used for the first training run.
canonical_corrected_26_1	Updated labelling with reduced region boundary artefacts. Recommended for new datasets.

The mask version to use is selected via the --masks flag in run_cortical_tiles.py (step 3) and must match the model version downloaded in step 5.

Configuration system

The submodule uses Hydra-style YAML configs located in champollion/configs/.

Training

This repository can be used to train models on already preprocessed data.

An end-to-end preprocessing from MRI, training, and evaluation is performed by the pipeline, with train_champollion.py from champollion_pipeline:

pixi run python3 src/train_champollion.py \
    /path/to/crops/2mm \
    --dataset dataset_name \
    --region SC-sylv_left

See champollion_pipeline documentation for the full training workflow.

For details on the training architecture, augmentations, and evaluation scripts, see champollion/README.rst.

Repository structure

champollion/
    champollion/
        configs/                    # Hydra-style YAML configs
        backbones/                  # CNN backbone definitions
        data/                       # Dataset and DataModule classes
        ssl_model.py                # SSL model
        evaluate.py                 # Embedding generation scripts
        train.py                    # Training entry point
    setup.cfg
    LICENSE

License

Released under the CeCILL-B license.