This is the official implementation of the [deepbet paper](https://arxiv.org/abs/2308.07003). 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)**  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 (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 ```bash 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 = {deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks}, Year = {2023} }