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https://github.com/ge-xing/SegMamba
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
https://github.com/ge-xing/SegMamba
Last synced: 3 months ago
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SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
- Host: GitHub
- URL: https://github.com/ge-xing/SegMamba
- Owner: ge-xing
- Created: 2024-01-23T08:08:08.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-05T03:57:48.000Z (7 months ago)
- Last Synced: 2024-10-17T06:24:06.391Z (3 months ago)
- Language: Python
- Homepage: https://arxiv.org/abs/2401.13560
- Size: 2.82 MB
- Stars: 344
- Watchers: 6
- Forks: 18
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
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README
# SegMamba
**Now we have open-sourced the pre-processing, training, inference, and metrics computation codes.**
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
[https://arxiv.org/abs/2401.13560](https://arxiv.org/abs/2401.13560)
Our advantage in speed and memory.
## Contact
If you have any questions about our project, please feel free to contact us by email at [email protected] or via WeChat at 18340097191.
## Environment install
Clone this repository and navigate to the root directory of the project.
```bash
git clone https://github.com/ge-xing/SegMamba.git
cd SegMamba
```
### Install causal-conv1d
```bash
cd causal-conv1d
python setup.py install
```
### Install mamba
```bash
cd mamba
python setup.py install
```
### Install monai
```bash
pip install monai
```
## Simple test
```bash
python 0_inference.py
```
## Preprocessing, training, testing, inference, and metrics computation
### Data downloading
Data is from [https://arxiv.org/abs/2305.17033](https://arxiv.org/abs/2305.17033)
Download from Baidu Disk [https://pan.baidu.com/s/1C0FUHdDtWNaYWLtDDP9TnA?pwd=ty22提取码ty22](https://pan.baidu.com/s/1C0FUHdDtWNaYWLtDDP9TnA?pwd=ty22)
Download from OneDrive [https://hkustgz-my.sharepoint.com/:f:/g/personal/zxing565_connect_hkust-gz_edu_cn/EqqaINbHRxREuIj0XGicY2EBv8hjwEFKgFOhF_Ub0mvENw?e=yTpE9B](https://hkustgz-my.sharepoint.com/:f:/g/personal/zxing565_connect_hkust-gz_edu_cn/EqqaINbHRxREuIj0XGicY2EBv8hjwEFKgFOhF_Ub0mvENw?e=yTpE9B)
### Preprocessing
In my setting, the data directory of BraTS2023 is : "./data/raw_data/BraTS2023/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/"
First, we need to run the rename process.
```bash
python 1_rename_mri_data.py
```
Then, we need to run the pre-processing code to do resample, normalization, and crop processes.
```bash
python 2_preprocessing_mri.py
```
After pre-processing, the data structure will be in this format:
### Training
When the pre-processing process is done, we can train our model.
We mainly use the pre-processde data from last step: **data_dir = "./data/fullres/train"**
```bash
python 3_train.py
```
The training logs and checkpoints are saved in:
**logdir = f"./logs/segmamba"**
### Inference
When we have trained our models, we can inference all the data in testing set.
```bash
python 4_predict.py
```
When this process is done, the prediction cases will be put in this path:
**save_path = "./prediction_results/segmamba"**
### Metrics computation
We can obtain the Dice score and HD95 on each segmentation target (WT, TC, ET for BraTS2023 dataset) using this code:
```bash
python 5_compute_metrics.py --pred_name="segmamba"
```
## Acknowledgement
Many thanks for these repos for their great contribution!
[https://github.com/MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)
[https://github.com/Project-MONAI/MONAI](https://github.com/Project-MONAI/MONAI)
[https://github.com/hustvl/Vim](https://github.com/hustvl/Vim)
[https://github.com/bowang-lab/U-Mamba](https://github.com/bowang-lab/U-Mamba)