# SegMamba & GliomaSAM3-MoE: Complete Reproduction Package

This repository contains **everything needed to reproduce** our brain tumor segmentation experiments on BraTS 2023 dataset, including source code, pre-trained weights, and evaluation results.

## Repository Structure

```
.
├── source_code/
│   ├── gliomasam3_moe/        # GliomaSAM3-MoE source code
│   ├── SegMamba/              # SegMamba source code (with mamba/monai)
│   └── sam3/                  # SAM3 dependency module
│
├── pretrained_weights/
│   └── sam3.pt                # SAM3 pretrained weights (3.3GB)
│
├── gliomasam3_moe/
│   ├── checkpoints/           # GliomaSAM3-MoE trained weights
│   │   ├── ckpt_step2000.pt
│   │   ├── ckpt_step2600.pt
│   │   └── ckpt_step3000.pt   # Best checkpoint
│   ├── configs/
│   │   └── train.yaml         # Training configuration
│   ├── eval_results/
│   │   ├── table4_et_absent.json   # ET presence classification results
│   │   └── table7_boundary_dice.json # Boundary-band Dice results
│   └── vis_res/               # Visualization results
│       ├── method_comparison/ # Side-by-side comparisons
│       ├── boundary/          # Boundary analysis figures
│       ├── moe_routing/       # MoE routing visualizations
│       └── ...
│
├── segmamba/
│   ├── checkpoints/           # SegMamba trained weights
│   │   ├── tmp_model_ep599_0.8295.pt
│   │   └── tmp_model_ep799_0.8498.pt  # Best checkpoint (Dice=0.8498)
│   └── prediction_results/
│       ├── segmamba_brats23_ep799/    # Prediction NIfTI files
│       └── result_metrics/            # Evaluation metrics
│
└── README.md
```

## Model Performance

### GliomaSAM3-MoE (ckpt_step3000)

**Boundary-band Dice (3-voxel band):**
| Region | Dice |
|--------|------|
| WT | 0.789 ± 0.057 |
| TC | 0.766 ± 0.154 |
| ET | 0.697 ± 0.161 |
| **Mean** | **0.750** |

**ET Presence Classification:**
| Metric | Value |
|--------|-------|
| AUROC | 0.896 |
| Accuracy | 0.795 |
| Sensitivity | 0.792 |
| Specificity | 1.000 |

### SegMamba (ep799)

- Mean Dice: 0.8498
- Trained for 800 epochs on BraTS 2023

## Quick Start: Reproduction

### 1. Download this repository

```bash
# Clone the dataset
git clone https://huggingface.co/datasets/ChipYTY/segmamba
cd segmamba
```

### 2. Prepare BraTS 2023 Data

Download BraTS 2023 GLI Challenge data from [Synapse](https://www.synapse.org/#!Synapse:syn51514105) and preprocess:

```bash
cd source_code/SegMamba
python 2_preprocessing_mri.py --input_dir /path/to/BraTS2023 --output_dir /path/to/processed
```

### 3. Run GliomaSAM3-MoE Inference

```bash
cd source_code/gliomasam3_moe

# Set SAM3 path
export PYTHONPATH=/path/to/source_code/sam3:$PYTHONPATH
export SAM3_CKPT=/path/to/pretrained_weights/sam3.pt

# Run inference
python infer.py \
    --config configs/train.yaml \
    --checkpoint /path/to/gliomasam3_moe/checkpoints/ckpt_step3000.pt \
    --data_dir /path/to/processed \
    --output_dir ./predictions
```

### 4. Run SegMamba Inference

```bash
cd source_code/SegMamba

python 4_predict.py \
    --checkpoint /path/to/segmamba/checkpoints/tmp_model_ep799_0.8498.pt \
    --data_dir /path/to/processed \
    --output_dir ./predictions
```

## Usage

### Loading GliomaSAM3-MoE

```python
import torch

# Load checkpoint
ckpt = torch.load("gliomasam3_moe/checkpoints/ckpt_step3000.pt", map_location="cpu")

# Model state dict is in ckpt["model"]
model.load_state_dict(ckpt["model"])
```

### Loading SegMamba

```python
import torch

# Load checkpoint
ckpt = torch.load("segmamba/checkpoints/tmp_model_ep799_0.8498.pt", map_location="cpu")

# Model state dict
model.load_state_dict(ckpt["model"])
```

## Data

Models were trained and evaluated on BraTS 2023 GLI Challenge dataset.

- **Download**: [Synapse BraTS 2023](https://www.synapse.org/#!Synapse:syn51514105)
- **Preprocessing**: Use `source_code/SegMamba/2_preprocessing_mri.py`

## Requirements

- Python 3.10+
- PyTorch 2.0+
- CUDA 11.8+ (for SegMamba's Mamba CUDA kernels)
- See `source_code/gliomasam3_moe/requirements.txt` for full list

## Citation

If you use these models, please cite the relevant papers.

## License

Please refer to the original model repositories for licensing information.