https://github.com/wwu-mmll/deepbet
Fast brain extraction using neural networks
https://github.com/wwu-mmll/deepbet
brain-extraction deep-learning neuroimaging python segmentation skull-stripping
Last synced: 6 months ago
JSON representation
Fast brain extraction using neural networks
- Host: GitHub
- URL: https://github.com/wwu-mmll/deepbet
- Owner: wwu-mmll
- License: mit
- Created: 2023-08-15T07:26:16.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-23T10:42:58.000Z (almost 2 years ago)
- Last Synced: 2025-11-30T23:40:39.944Z (7 months ago)
- Topics: brain-extraction, deep-learning, neuroimaging, python, segmentation, skull-stripping
- Language: Python
- Homepage:
- Size: 28.1 MB
- Stars: 42
- Watchers: 3
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
This is the official implementation of the [deepbet paper](https://www.sciencedirect.com/science/article/pii/S0010482524009302?dgcid=author).
deepbet is a neural network based tool, which achieves state-of-the-art results for brain extraction of T1w MR images
of healthy adults, while taking ~1 second per image.
## Usage
After installation, there are three ways to use deepbet
1. ```deepbet-gui``` runs the **Graphical User Interface (GUI)**
2. ```deepbet-cli``` runs the **Command Line Interface (CLI)**
```bash
deepbet-cli -i /path/to/inputs -o /path/to/output/brains
```
3. Run deepbet directly in Python
```python
from deepbet import run_bet
input_paths = ['path/to/sub_1/t1.nii.gz', 'path/to/sub_2/t1.nii.gz']
brain_paths = ['path/to/sub_1/brain.nii.gz', 'path/to/sub_2/brain.nii.gz']
mask_paths = ['path/to/sub_1/mask.nii.gz', 'path/to/sub_2/mask.nii.gz']
tiv_paths = ['path/to/sub_1/tiv.csv', 'path/to/sub_2/tiv.csv']
run_bet(input_paths, brain_paths, mask_paths, tiv_paths, threshold=.5, n_dilate=0, no_gpu=False)
```
Besides the `input paths` and the output paths
- `brain_paths`: Destination filepaths of input nifti **files with brain extraction applied**
- `mask_paths`: Destination filepaths of **brain mask nifti files**
- `tiv_paths`: Destination filepaths of **.csv-files containing the total intracranial volume (TIV)** in cm³
- Simpler than it sounds: TIV = Voxel volume * Number of 1-Voxels in brain mask
you can additionally do
- **Fine adjustments** via `threshold`: deepbet internally predicts values between 0 and 1 for each voxel and then includes each voxel which is above 0.5.
You can change this threshold (e.g. to 0.1 to include more voxels).
- **Coarse adjustments** via `n_dilate`: Enlarges/shrinks mask by successively adding/removing voxels adjacent to mask surface.
and choose if you want to **use GPU (only NVIDIA supported) for speedup**
- `no_gpu`: deepbet automatically uses the NVIDIA GPU if available. If you do not want that, set no_gpu=True.
## Installation
For accelerated processing via GPU, it is recommended to first install PyTorch separately via a [command customized for your system](https://pytorch.org/get-started/locally/).
Then the package itself can be installed via
```bash
pip install deepbet
```
Due to [this issue](https://github.com/ContinuumIO/anaconda-issues/issues/6833), the GUI can look ugly, which can be resolved via
```bash
conda install -c conda-forge tk=*=xft_*
```
## Citation
If you find this code useful in your research, please consider citing
```bibtex
@article{deepbet,
title = {deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks},
journal = {Computers in Biology and Medicine},
volume = {179},
pages = {108845},
year = {2024},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2024.108845},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524009302},
author = {Lukas Fisch and Stefan Zumdick and Carlotta Barkhau and Daniel Emden and Jan Ernsting and Ramona Leenings and Kelvin Sarink and Nils R. Winter and Benjamin Risse and Udo Dannlowski and Tim Hahn},
}
```