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VeinSeg

Physics-informed deep learning for cerebral vein segmentation from QSM or R2*.

Hugging Face | GitHub


Overview

VeinSeg segments cerebral veins from Quantitative Susceptibility Mapping (QSM) or R2* maps, using only the image as input. It supports multi-site, multi-field-strength data (3T and 7T) across five QSM reconstruction methods (TGV, MEDI, L1, STAR, iLSQR) and R2* maps using a physics-informed training objective.


Installation

Install PyTorch first (pytorch.org), then:

pip install veinseg-qsm

Download the model weights (~290 MB, once only):

veinseg-install /path/to/models/dir

On shared HPC clusters, set:

export VEINSEG_CHECKPOINT=/shared/models/veinseg/checkpoint.pth

Usage

veinseg -i qsm.nii.gz -r tgv -f 7t -o mask.nii.gz -p prob.nii.gz

Required arguments

Flag Description
-i QSM susceptibility map (.nii / .nii.gz, ppm), or R2* map (1/s) with -r r2star — the only input needed
-r Reconstruction method: tgv | medi | l1 | star | ilsqr | r2star
-f MRI field strength: 7t | 3t
-o Output binary vein mask
-p Output vein probability map

Optional arguments

Flag Default Description
--threshold 0.5 Probability threshold for binary mask
--step-size 0.5 Sliding window overlap as fraction of patch
--no-tta off Disable test-time augmentation (mirroring)
--device auto auto | cpu | cuda

Examples

# QSM
veinseg -i qsm.nii.gz -r medi -f 3t -o mask.nii.gz -p prob.nii.gz

# R2*
veinseg -i r2star.nii.gz -r r2star -f 3t -o mask.nii.gz -p prob.nii.gz

# CPU inference, no test-time augmentation (faster)
veinseg -i qsm.nii.gz -r tgv -f 7t -o mask.nii.gz -p prob.nii.gz \
        --device cpu --no-tta

License

Apache 2.0

Metadata

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