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ilex-hub/lifespan_strip.1
lifespan_strip.1 is a machine learning model from ilex-hub. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for ilex. The card lists the license as mit.
CNN+ViT+STN+flow composite that performs age-aware brain extraction by registering an age-specific atlas onto a subject T1w MRI. Wang et al. (Nat Biomed Eng 9:700-715, 2025) the standalone "Atlases-empowered Lifespan…
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From the Hugging Face model README
CNN+ViT+STN+flow composite that performs age-aware brain extraction by registering an age-specific atlas onto a subject T1w MRI. Wang et al. (Nat Biomed Eng 9:700-715, 2025) the standalone "Atlases-empowered Lifespan Skull Stripping" model from DBC-Lab (UNC). Architecture: three-scale ViT trunks (hidden=768/128/16, num_heads=12/8/4) feeding a CNN-encoder/decoder U-Net (channels 16->32->64->128->256, in-house UnetBasicBlock variant with InstanceNorm3d(affine=False), no residual skip), plus a rigid STN (Conv stack + 71M-param FC layer + manual cos/sin Euler-angle matrix assembly) and a VoxelMorph-style flow head (Conv encoder/decoder + VecInt scaling-and-squaring SVF integration + dense warp via map_coordinates). 159M params total; largest single block is the FC localizer (256 x 13**3 -> 128 features = 72M params). Age-awareness is achieved at inference by selecting one of 11 atlas pairs (Month0/3/6/9/12/18/24/Adolescent/ Adult/Elder/Year65-80) based on a user-supplied age_in_month; a single shared set of network weights covers the full lifespan. Atlases ship via lytemaps as TemplateFlow-spec data, not in the ilex code repo. The two-pass inference pipeline (coarse mask -> crop + center -> refine) lives in ilex.models.lifespan_strip.pipeline.
Brain-extract a 3D T1-weighted MRI volume conditional on an age-specific atlas. Input is a single-channel 128x128x128 volume at 2 mm isotropic, intensity in [0, 1], plus the matching atlas image + binary mask at the same spec. The caller selects one of 11 atlas buckets (Month0/3/6/9/12/18/24/Adolescent/Adult/Elder/Year65-80) based on the subject's age. Output is a six-tuple per NET.forward (logits, moving_trans, y_source, pos_flow, x_reg, atlas_mask_formable); the primary output is logits[1] (soft brain probability), thresholded at 0.5 after the two-pass pipeline for the binary mask. The preprocessing pipeline (RAI orient + N4 + resample + histogram-match + min-max) is the caller's concern (delegated to ilex.models.lifespan_strip.pipeline).
from ilex.models.lifespan_strip import LifespanStrip
model = LifespanStrip.from_pretrained('ilex-hub/lifespan_strip.1')
Li Wang, Yue Sun, Jakob Seidlitz, Richard A. I. Bethlehem, Aaron Alexander-Bloch, Lena Dorfschmidt, Gang Li, Jed T. Elison, Weili Lin
Wang L., Sun Y., Seidlitz J., Bethlehem R. A. I., Alexander-Bloch A., Dorfschmidt L., Li G., Elison J. T., Lin W., Wang L. (2025). A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases. Nature Biomedical Engineering 9:700-715.
HF Hub license tag: mit
Effective terms: MIT (Developing Brain Computing Lab, UNC -- Wang et al.) on both the network code and the Lifespan_Skull_Stripping.pt weights. The 11 age-specific atlas pairs are shipped via lytemaps under the same MIT terms; we host them on OSF as TemplateFlow-spec data, not in the ilex repo. The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0.
Upstream license reference: https://opensource.org/licenses/MIT
Network architecture and pretrained weights -- copyright (c) Developing Brain Computing Lab (DBC-Lab, UNC), released under the MIT license. JAX / Equinox port code -- copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.
Original weights / reference implementation: https://github.com/DBC-Lab/Atlases-empowered_Lifespan_Skull_Stripping
This artefact was produced by ilex's
save/load pipeline. The architecture is implemented in
ilex.models.lifespan_strip.LifespanStrip and the weights have been converted
from their upstream format. See the upstream source above
for the canonical reference.