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# GliomaSAM3-MoE (Minimal BraTS2023 3D Segmentation)
This is a **minimal, fully runnable** BraTS2023 3D segmentation project with a complete
`GliomaSAM3_MoE` model, synthetic data support, training, inference, and tests.
## Install
```bash
pip install -r requirements.txt
```
## Run tests
```bash
pytest -q
```
## Synthetic debug training (no real data required)
```bash
python train.py --config configs/debug.yaml --synthetic true
```
## Real BraTS data layout (expected)
Each case is a folder under `data.root_dir`, containing:
```
case_id/
t1n.nii.gz
t1c.nii.gz
t2f.nii.gz
t2w.nii.gz
seg.nii.gz
```
Label values must be in `{0, 1, 2, 4}`.
## SegMamba preprocessed data (npz)
If you use the SegMamba preprocessing pipeline, place `*.npz` under:
```
./data/fullres/train
```
This project supports that format with `data.format: "segmamba_npz"` (already in configs).
It will read `*.npz` and cached `*.npy` / `*_seg.npy`, and automatically map label `3 -> 4`.
Recommended paths (aligned with SegMamba):
- checkpoints: `./logs/segmamba/model`
- predictions: `./prediction_results/segmamba`
Example:
```bash
python train.py --config configs/train.yaml
python infer.py --config configs/train.yaml --input ./data/fullres/train --checkpoint ./logs/segmamba/model/ckpt_stepXXXX.pt --output ./prediction_results/segmamba
```
## Inference
```bash
python infer.py --config configs/train.yaml --input /path/to/case_or_root --checkpoint /path/to/ckpt.pt --output ./outputs
```
Outputs:
- `*_regions_prob.nii.gz` : probability maps for [WT, TC, ET]
- `*_regions_bin.nii.gz` : thresholded binary maps
- `*_label.nii.gz` : final label map in `{0,1,2,4}`
When `data.format: "segmamba_npz"`, `infer.py` also writes:
- `{case_id}.nii.gz` : 3-channel (TC/WT/ET) mask for SegMamba `5_compute_metrics.py`
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