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# 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.
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