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ilex-hub/
tract_seg.tract-segmentation-v3.1

Name: tract_seg.tract-segmentation-v3.1
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TractSeg -- white matter bundle / endings segmentation (2D U-Net + 3-axis ensemble) -- TractSeg tract_segmentation v3 (42 bundles)

Description

JAX / Equinox port of TractSeg (Wasserthal et al., NeuroImage 2018; github.com/MIC-DKFZ/TractSeg). A 2D U-Net with deep supervision that, per slice, predicts binary masks for each white-matter bundle (or each bundle's endpoint regions) from a 9-channel FOD-peak input volume. The upstream's canonical inference path runs the per-slice forward on all three principal slice orientations (axial, coronal, sagittal) and averages the three resulting 3D mask predictions; the ilex TractSegPipeline class wraps that end to end.

Two task variants ship in v0:

tract_segmentation_v3 (42 bundles, the upstream's current default)
endings_segmentation_v4 (72 endpoint regions)

Both share parameter-identical bodies (~37 M trainable scalars); only the three 1x1 head Conv2ds at output_2, output_3, and conv_5 differ in out_channels to match the task's n_classes.

Intended use

Predict per-bundle binary masks for 42 white-matter bundles from a 9-channel FOD-peak input volume. The TractSegPipeline 3-axis ensemble produces a 4D (42, D, H, W) sigmoid probability volume; consumers threshold + run downstream tractography filtering. Zenodo record 3634539, epoch 266.

Usage

python

from ilex.models.tract_seg import TractSeg
model = TractSeg.from_pretrained('ilex-hub/tract_seg.tract-segmentation-v3.1')

Authors

Wasserthal J., Neher P. F., Maier-Hein K. H. (MIC-DKFZ, German Cancer Research Center, Heidelberg University)

Citation

Wasserthal J., Neher P. F., Maier-Hein K. H. (2018). TractSeg -- Fast and accurate white matter tract segmentation. NeuroImage 183, 239-253. doi:10.1016/j.neuroimage.2018.07.070.

References

Wasserthal J., Neher P. F., Maier-Hein K. H. (2018). TractSeg -- Fast and accurate white matter tract segmentation. NeuroImage 183, 239-253. doi 10.1016/j.neuroimage.2018.07.070.
Wasserthal J., Neher P. F., Hirjak D., Maier-Hein K. H. (2020). Multiparametric mapping of white matter microstructure in 100,000 subjects from the UK Biobank.
Upstream code, license, training scripts -- github.com/MIC-DKFZ/TractSeg (Apache-2.0).
Upstream weights -- Zenodo records 3634539 (tract_segmentation v3, epoch 266) and 3518348 (endings_segmentation v4, epoch 220).

License

HF Hub license tag: apache-2.0

Effective terms: Apache-2.0 (MIC-DKFZ) on both the network code (github.com/MIC-DKFZ/TractSeg) and the pretrained weights (Zenodo records 3634539 and 3518348). The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0.

Upstream license reference: https://www.apache.org/licenses/LICENSE-2.0

Copyright

TractSeg is copyright (c) MIC-DKFZ (German Cancer Research Center), Apache-2.0-licensed on both the network code (github.com/MIC-DKFZ/TractSeg) and the pretrained weights (Zenodo records 3634539 + 3518348). The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0.

Upstream source

Original weights / reference implementation: https://github.com/MIC-DKFZ/TractSeg

Provenance

This artefact was produced by ilex's save/load pipeline. The architecture is implemented in ilex.models.tract_seg.TractSeg and the weights have been converted from their upstream format. See the upstream source above for the canonical reference.