fastsurfer_cnn.coronal.1

FastSurferCNN (v1, pre-VINN) -- Coronal-plane parcellator (79 classes)

Description

FastSurferCNN is a per-slice 2D Competitive-Dense-UNet for whole-brain parcellation, the pre-VINN "v1" architecture introduced in Henschel et al. 2020. This ilex bundle ports the FastSurfer release at GitHub commit 070647d (Jun 2022), which is the version DeepPrep pins to. The full triplanar parcellation pipeline combines three independently-trained checkpoints (axial, coronal, sagittal); each sees per-slice 7-thick-slab inputs of shape (7, 256, 256) and predicts per-pixel class logits over a 79-class space (axial / coronal) or 51-class space (sagittal, where left/right hemispheric labels collapse and are recovered downstream). The architecture comprises four CompetitiveEncoderBlocks (the first with an input-variant BN layer), a CompetitiveDenseBlock bottleneck, four CompetitiveDecoderBlocks with skip-connection-Maxout fusion, and a 1x1 classifier Conv2d. The per-block "Maxout" gates take elementwise max of the BN-normalised activation against the previous Maxout output (or the raw input, for the first gate), and the encoder / decoder are coupled via MaxPool / MaxUnpool with stored argmax indices.

Intended use

Per-slice 2D parcellation along the coronal plane. Same architecture as the axial variant; pair with axial + sagittal via parcellate() for triplanar view-aggregated parcellation.

Usage

from ilex.models.fastsurfer_cnn import FastSurferCNN
model = FastSurferCNN.from_pretrained('ilex-hub/fastsurfer_cnn.coronal.1')

Authors

Henschel L., Conjeti S., Estrada S., Diers K., Fischl B., Reuter M.

Citation

Henschel L., Conjeti S., Estrada S., Diers K., Fischl B., Reuter M. (2020). FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219: 117012. doi:10.1016/j.neuroimage.2020.117012

References

• Henschel L., Conjeti S., Estrada S., Diers K., Fischl B., Reuter M. (2020). FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219:117012. doi:10.1016/j.neuroimage.2020.117012
• Roy A. G., Conjeti S., Sheet D., Katouzian A., Navab N., Wachinger C. (2017). Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data. MICCAI 2017 LNCS 10435:231-239. doi:10.1007/978-3-319-66179-7_27 (the Competitive-Dense-UNet design that FastSurferCNN extends)

License

HF Hub license tag: apache-2.0

Effective terms: Apache-2.0 (Image Analysis Lab, German Center for Neurodegenerative Diseases (DZNE), Bonn) on both the network code and the pretrained weights at commit 070647d. No commercial restrictions; no gating required.

Upstream license reference: https://github.com/Deep-MI/FastSurfer/blob/070647d/LICENSE

Copyright

Network architecture and training code: copyright (c) Image Analysis Lab, German Center for Neurodegenerative Diseases (DZNE), Bonn, distributed under the Apache-2.0 license at https://github.com/Deep-MI/FastSurfer. Pretrained weights (checkpoints/Axial_Weights_FastSurferCNN, checkpoints/Coronal_Weights_FastSurferCNN, checkpoints/Sagittal_Weights_FastSurferCNN, all under Epoch_30_training_state.pkl) ship from the same Apache-2.0 repository and inherit the same license. The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0 for the port code itself; the ilex license does not override the upstream Apache-2.0 terms governing the pretrained weights.

Upstream source

Original weights / reference implementation: https://github.com/Deep-MI/FastSurfer/tree/070647d

Provenance

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