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Dataset507_TopBrainTA36CT/nnUNetTrainerTopBrain_dice_ce_skelrec__ptPlans__openmind-resencl-vf-ct____Spacing__0.70_0.48_0.48___Norm__Z_Z__3d_fullres/dataset.json

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+    "name": "Dataset507_TopBrainTA36CT",
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+}

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Dataset507_TopBrainTA36CT/nnUNetTrainerTopBrain_dice_ce_skelrec__ptPlans__openmind-resencl-vf-ct____Spacing__0.70_0.48_0.48___Norm__Z_Z__3d_fullres/fold_0/checkpoint_best.pth

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Dataset508_TopBrainTA36MR/nnUNetTrainerTopBrain_dice_ce_skelrec__ptPlans__openmind-resencl-vf-mr____Spacing__0.60_0.30_0.30___Norm__Z__3d_fullres/dataset.json

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Dataset508_TopBrainTA36MR/nnUNetTrainerTopBrain_dice_ce_skelrec__ptPlans__openmind-resencl-vf-mr____Spacing__0.60_0.30_0.30___Norm__Z__3d_fullres/dataset_fingerprint.json

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README.md

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+---
+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/USER/REPO)**, which applies the
+preprocessing below for you:
+
+```bash
+pip install git+https://github.com/USER/REPO
+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.

release_manifest.json

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+{
+  "models": [
+    {
+      "dataset": "Dataset507_TopBrainTA36CT",
+      "configuration": "nnUNetTrainerTopBrain_dice_ce_skelrec__ptPlans__openmind-resencl-vf-ct____Spacing__0.70_0.48_0.48___Norm__Z_Z__3d_fullres",
+      "folds": [
+        {
+          "fold": "fold_0",
+          "checkpoint": "checkpoint_best.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_1",
+          "checkpoint": "checkpoint_best.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_2",
+          "checkpoint": "checkpoint_best.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_3",
+          "checkpoint": "checkpoint_best.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_4",
+          "checkpoint": "checkpoint_best.pth",
+          "mb": 409.8
+        }
+      ]
+    },
+    {
+      "dataset": "Dataset508_TopBrainTA36MR",
+      "configuration": "nnUNetTrainerTopBrain_dice_ce_skelrec__ptPlans__openmind-resencl-vf-mr____Spacing__0.60_0.30_0.30___Norm__Z__3d_fullres",
+      "folds": [
+        {
+          "fold": "fold_0",
+          "checkpoint": "checkpoint_final.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_1",
+          "checkpoint": "checkpoint_final.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_2",
+          "checkpoint": "checkpoint_final.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_3",
+          "checkpoint": "checkpoint_final.pth",
+          "mb": 409.8
+        },
+        {
+          "fold": "fold_4",
+          "checkpoint": "checkpoint_final.pth",
+          "mb": 409.8
+        }
+      ]
+    }
+  ],
+  "excluded": [
+    "validation",
+    "summary.json",
+    "debug.json",
+    "progress.png"
+  ],
+  "checkpoint_keys": [
+    "network_weights",
+    "trainer_name",
+    "inference_allowed_mirroring_axes"
+  ],
+  "total_mb_before": 8198.8,
+  "total_mb_after": 4097.6
+}