Fast brain extraction using neural networks
Project description
This is the official implementation of the deepbet paper.
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
deepbet-gui
runs the Graphical User Interface (GUI)
deepbet-cli
runs the Command Line Interface (CLI)
- Run deepbet directly in 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 appliedmask_paths
: Destination filepaths of brain mask nifti filestiv_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 (NVIDIA and Apple M1/M2 support) for speedup
no_gpu
: deepbet automatically uses NVIDIA GPU or Apple M1/M2 if available. If you do not want that set no_gpu=True.
Installation
pip install deepbet
conda install -c anaconda pyqt=5.15.7
Citation
If you find this code useful in your research, please consider citing:
@inproceedings{deepbet,
Author = {Lukas Fisch, Stefan Zumdick, Carlotta Barkhau, Daniel Emden, Jan Ernsting, Ramona Leenings, Kelvin Sarink, Nils R. Winter, Udo Dannlowski, Tim Hahn},
Title = {fastbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks},
Journal = {Imaging Neuroscience},
Year = {2023}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
deepbet-0.0.2.tar.gz
(10.1 MB
view hashes)
Built Distribution
deepbet-0.0.2-py3-none-any.whl
(10.2 MB
view hashes)