ToPBrain TA36 — cerebral artery segmentation
36-class segmentation of the cerebral arteries from CT angiography or MR angiography / TOF. nnU-Net v2, ResEnc-L encoder, 5-fold ensemble per modality.
Run these weights with topbrain-infer, which applies the preprocessing below for you:
pip install git+https://github.com/ignacio-ms/topbrain
topbrain-infer -i scan.nii.gz -o out/ --hf-repo THIS/REPO
Models
| Directory | Modality | Input channels | Folds | Checkpoint |
|---|---|---|---|---|
Dataset507_TopBrainTA36CT | CT | 2 | 5 | checkpoint_best.pth |
Dataset508_TopBrainTA36MR | MR | 1 | 5 | checkpoint_final.pth |
Preprocessing — required, and not recoverable from the weights
Both models were trained on volumes already mapped onto [0, 1], and nnU-Net's own
normalisation is calibrated on that result. Feeding raw Hounsfield units or raw TOF units
produces a confident, wrong segmentation rather than an error.
CT — two input channels, the same volume windowed twice:
| Channel | Window | Purpose |
|---|---|---|
_0000 | -100 to 1500 HU, clipped, mapped to [0, 1] | Wide anatomical context |
_0001 | 300 to 600 HU, clipped, mapped to [0, 1] | Narrow window on the contrast-filled lumen |
MR / TOF — one input channel: percentiles 0.5 and
99.5, measured per volume over non-zero voxels only, mapped onto
[0, 1]. Per-volume because MR units are arbitrary; non-zero only because TOF stores air as
exactly 0 over most of the field of view, which would otherwise pin the low percentile at 0.
Test-time augmentation is off: inference_allowed_mirroring_axes is null in every checkpoint,
because the classes are lateralised and mirroring would average R-ICA with L-ICA.
Labels
36 foreground classes, identical for both models. 0 is background. R-/L- are
the patient's right and left; the internal carotid is split into supraclinoid (4 / 6) and
infraclinoid (35 / 36) segments.
| 1 BA | 2 R-P1P2 | 3 L-P1P2 | 4 R-ICA-C6-C7 | 5 R-M1 | 6 L-ICA-C6-C7 |
| 7 L-M1 | 8 R-Pcom | 9 L-Pcom | 10 Acom | 11 R-A1A2 | 12 L-A1A2 |
| 13 R-A3 | 14 L-A3 | 15 3rd-A2 | 16 3rd-A3 | 17 R-M2 | 18 R-M3 |
| 19 L-M2 | 20 L-M3 | 21 R-P3P4 | 22 L-P3P4 | 23 R-VA | 24 L-VA |
| 25 R-SCA | 26 L-SCA | 27 R-AICA | 28 L-AICA | 29 R-PICA | 30 L-PICA |
| 31 R-AChA | 32 L-AChA | 33 R-OA | 34 L-OA | 35 R-ICA-C1-C5 | 36 L-ICA-C1-C5 |
What is in these files
Checkpoints are stripped to what inference reads — network_weights, trainer_name,
init_args['configuration'], inference_allowed_mirroring_axes — which halves them from 820 MB
to 410 MB per fold. Predictions are bit-identical to the training checkpoints. Optimizer state,
training logs, and per-case validation outputs are not included.
Intended use and limitations
Research use only. Not a medical device and not for clinical decision-making. Trained on research angiography cohorts; behaviour on scanners, contrast protocols, pathology or age groups outside those cohorts is unvalidated. The rarer distal and cerebellar classes are the least reliable. Always inspect the output before relying on it.