veinseg-qsm 1.0.3

Physics-informed cerebral vein segmentation from QSM

VeinSeg

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

Hugging Face | GitHub

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Overview

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

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Installation

Install PyTorch first (pytorch.org), then:

pip install veinseg

Download the model weights (~600 MB, once only):

veinseg-install /path/to/models/dir

On shared HPC clusters, set:

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

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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)
-r	QSM reconstruction method: tgv | medi | l1 | star | ilsqr
-f	MRI field strength: 7t | 3t
-o	Output binary vein mask
-p	Output vein probability map

Optional arguments

Flag	Default	Description
--local-field PATH	—	Measured background-removed local field (skips dipole computation)
--local-field-units	auto	hz | ppm | auto
--b0 X Y Z	0 0 1	B0 direction in world/scanner axes
--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
--device	auto	auto | cpu | cuda
--out-field PATH	—	Save local field channel used (ppm)
--out-frangi PATH	—	Save Frangi vesselness channel

Examples

# Dipole field computed automatically from QSM
veinseg -i qsm.nii.gz -r tgv -f 7t -o mask.nii.gz -p prob.nii.gz

# With measured local field (Romeo output in Hz — auto-detected)
veinseg -i qsm.nii.gz -r medi -f 7t -o mask.nii.gz -p prob.nii.gz \
        --local-field bgrm_field.nii.gz

# Inspect intermediate channels
veinseg -i qsm.nii.gz -r tgv -f 7t -o mask.nii.gz -p prob.nii.gz \
        --out-field dipole_field.nii.gz --out-frangi frangi.nii.gz

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License

Apache 2.0