WholeBrain UNesT (133-class T1 brain parcellation) -- WholeBrain UNesT (MONAI wholeBrainSeg_Large_UNEST v0.2.7)
Description
WholeBrain UNesT, ported to JAX / Equinox from MONAI's wholeBrainSeg_Large_UNEST_segmentation bundle. UNesT pairs a Nested Transformer (NesT) 3D encoder -- which partitions each feature map into non-overlapping cubic blocks, applies transformer layers with attention localised within each block, and aggregates blocks between hierarchy levels via a strided conv-pool -- with a UNETR-style convolutional decoder. The network parcellates a T1 brain MRI patch into 133 whole-brain regions.
Intended use
Whole-brain parcellation of a T1-weighted brain MRI into 133 regions. Input is a single-channel 96x96x96 patch, nonzero channel-wise z-score normalised; output is 133-channel raw logits (argmax over the channel axis for the label map). The bundle tiles a full brain volume with sliding-window inference (roi 96^3, overlap 0.7) -- an out-of-model inference concern not vendored here.
Usage
1from ilex.models.wholebrain_unest import WholeBrainUNesT
2model = WholeBrainUNesT.from_pretrained('ilex-hub/wholebrain_unest.1')
Authors
Yu X., Yang Q., Zhou Y., Cai L. Y., Gao R., Lee H. H., et al.
Citation
Yu X., Yang Q., Zhou Y., Cai L. Y., Gao R., Lee H. H., et al. (2023). UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation. Medical Image Analysis. arXiv:2209.14378. NesT encoder: Zhang Z., et al. (2022), AAAI, arXiv:2105.12723.
References
- Yu X., Yang Q., Zhou Y., et al. (2023). UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation. Medical Image Analysis. arXiv:2209.14378.
- Zhang Z., Zhang H., Zhao L., Chen T., Arik S. O., Pfister T. (2022). Nested Hierarchical Transformer (NesT). AAAI 2022. arXiv:2105.12723.
- Bundle: https://huggingface.co/MONAI/wholeBrainSeg_Large_UNEST_segmentation
License
HF Hub license tag: apache-2.0
Effective terms: Apache-2.0 (MONAI Consortium / MASI Lab, Vanderbilt) on both the UNesT network code and the wholeBrainSeg_Large_UNEST_segmentation bundle weights. The NesT encoder is adapted from the Nested Transformer (Zhang et al. 2022). No commercial restrictions; no gating required. The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0.
Copyright
Network architecture and pretrained weights: copyright (c) the MONAI Consortium / MASI Lab (Vanderbilt), released under the Apache-2.0 License. The NesT encoder is adapted from the Nested Transformer (Zhang et al. 2022). JAX / Equinox port: copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.
Upstream source
Provenance
This artefact was produced by
ilex's
save/load pipeline. The architecture is implemented in
ilex.models.wholebrain_unest.WholeBrainUNesT and the weights have been converted
from their upstream format. See the upstream source above
for the canonical reference.
