| --- |
| license: other |
| license_name: topbrain-research-use |
| tags: |
| - medical |
| - image-segmentation |
| - nnunet |
| - angiography |
| pipeline_tag: image-segmentation |
| library_name: topbrain-infer |
| --- |
| |
| # 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](https://github.com/ignacio-ms/topbrain)**, which applies the |
| preprocessing below for you: |
|
|
| ```bash |
| 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. |
|
|
