Awesome

HD-BET

This repository provides easy to use access to our HD-BET brain extraction tool. HD-BET is the result of a joint project between the Department of Neuroradiology at the Heidelberg University Hospital, the Divison for Computational Radiology & Clinical AI, University Hospital Bonn and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ).

If you are using HD-BET, please cite the following publication:

Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A,
Schlemmer HP, Heiland S, Wick W, Bendszus M, Maier-Hein KH, Kickingereder P.
Automated brain extraction of multi-sequence MRI using artificial neural
networks. Hum Brain Mapp. 2019; 1–13. https://doi.org/10.1002/hbm.24750

Compared to other commonly used brain extraction tools, HD-BET has some significant advantages:

HD-BET was developed with MRI-data from a large multicentric clinical trial in adult brain tumor patients acquired across 37 institutions in Europe and included a broad range of MR hardware and acquisition parameters, pathologies or treatment-induced tissue alterations. We used 2/3 of data for training and validation and 1/3 for testing. Moreover independent testing of HD-BET was performed in three public benchmark datasets (NFBS, LPBA40 and CC-359).
HD-BET was trained with precontrast T1-w, postcontrast T1-w, T2-w and FLAIR sequences. It can perform independent brain extraction on various different MRI sequences and is not restricted to precontrast T1-weighted (T1-w) sequences. Other MRI sequences may work as well (just give it a try!)
HD-BET was designed to be robust with respect to brain tumors, lesions and resection cavities as well as different MRI scanner hardware and acquisition parameters.
HD-BET outperformed five publicly available brain extraction algorithms (FSL BET, AFNI 3DSkullStrip, Brainsuite BSE, ROBEX and BEaST) across all datasets and yielded median improvements of +1.33 to +2.63 points for the DICE coefficient and -0.80 to -2.75 mm for the Hausdorff distance (Bonferroni-adjusted p<0.001).
HD-BET is very fast on GPU with <5s run time per MRI sequence. Even on CPU it is not slower than other commonly used tools.

Installation Instructions

Note that you need to have a python3 installation for HD-BET to work. HD-BET was extensively tested in Linux but should work on Windows and Mac as well!

We recommend installing HD-BET in a virtual environment.

Install HD-BET either as a python package (recommended):

pip install hd-bet

(Alternatively) install the most recent master from GitHub

Clone this repository:

git clone https://github.com/MIC-DKFZ/HD-BET

Go into the repository (the folder with the pyproject.toml file) and install:
```
pip install -e .
```
Per default, model parameters will be downloaded to ~/hd-bet_params. If you wish to use a different folder, open HD_BET/paths.py in a text editor and modify folder_with_parameter_files

How to use it

Using HD-BET is straightforward. You can use it in any terminal on your linux system. The hd-bet command was installed automatically. We provide GPU as well as MPS and CPU support. Running on GPU/MPS is a lot faster and should always be preferred. Here is a minimalistic example of how you can use HD-BET:

hd-bet -i INPUT_FILENAME -o OUTPUT_FILENAME

INPUT_FILENAME must be a nifti (.nii.gz) file containing 3D MRI image data. 4D image sequences are not supported (however can be split upfront into the individual temporal volumes using fslsplit1). INPUT_FILENAME can be any MRI sequence. Pre-, postcontrast T1-w, T2-w and FLAIR were used for training and should work best. Other sequences will most likely work as well. Input images must match the orientation of standard MNI152 template! Use fslreorient2std 2 upfront to ensure that this is the case.

By default, HD-BET will run in GPU mode and use test time data augmentation by mirroring along all axes.

For batch processing it is faster to process an entire folder at once as this will mitigate the overhead of loading and initializing the model for each case:

hd-bet -i INPUT_FOLDER -o OUTPUT_FOLDER

The above command will look for all nifti files (*.nii.gz) in the INPUT_FOLDER and save the brain masks under the same name in OUTPUT_FOLDER.

GPU is nice, but I don't have one of those... What now?

HD-BET has CPU support. Running on CPU takes a lot longer though and you will need quite a bit of RAM. To run on CPU, we recommend you use the following command:

hd-bet -i INPUT_FOLDER -o OUTPUT_FOLDER -device cpu --disable_tta

This works of course also with just an input file:

hd-bet -i INPUT_FILENAME -device cpu --disable_tta

The option --disable_tta will disable test time data augmentation (speedup of 8x).

HD-BET should also run on mps, just specify -device mps

More options:

For more information, please refer to the help functionality:

hd-bet -h

FAQ

Will you provide the training code? It's basically nnU-Net since HD-BET v2. We use nnUNetTrainerDA5 with minor (not yet published) modifications.
What run time can I expect on CPU/GPU? This depends on your MRI image size. Typical run times (preprocessing and resampling included) are just a couple of seconds for GPU and about 2 Minutes on CPU (using --disable_tta)

1https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils

2https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained