#HD-BET

This repository provides easy to use access to our recently published HD-BET brain extraction tool. HD-BET is the result of a joint project between the Department of Neuroradiology at the Heidelberg University Medical Center and the Division of Medical Image Computing at the German Cancer Research Center.

If you are using HD-BET, please cite the following publication: (Citation will appear tomorrow, as we are uploading to arxiv today...)

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

• HD-BET can run brain extraction on the most commom MRI sequences natively and is not restricted to T1w! It was trained with T1w, T1w with contrast enhancement, T2w and FLAIR sequences. Other MRI sequences may work as well (just give it a try!)
• it was designed to be robust with respect to brain tumors, lesions and resection cavities
• it is very fast on GPU with <10s run time per MRI sequence. Even on CPU it is not slower than other commonly used tools

##Installation Instructions

1. Clone this repository
2. Go into the repository (the folder with the setup.py file) and install with

   pip install -e .
   

3. 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

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 CPU as well as GPU support. Running on GPU is a lot faster though and should always be preferred. Here is a minimalistic example of how you can use HD-BET (you need to be in the HD_BET directory)

hd-bet -i INPUT_FILENAME

INPUT_FILENAME must be a nifti (.nii.gz) file containing 3D image data. 4D image sequences are not supported. INPUT_FILENAME can be either T1w, T1w with contrast agent, T2w or FLAIR MRI image. Other modalities might work as well. Input images must match the orientation of MNI152! Use fslreorient2std ¹ to ensure that is the case!

By default, this will run in GPU mode, use the parameters of all five models (which originate from a five-fold cross-validation), use test time data augmentation by mirroring along all axes and not do any postprocessing.

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.

To modify the device (CPU/GPU), whether to use test time data augmentation and postprocessing please refer to the documentation of run.py:

hd-bet --help

1. How much GPU memory do I need to run HD-BET?
   We ran all our experiments on NVIDIA Titan X GPUs with 12 GB memory. For inference you will need less, but since inference in implemented by exploiting the fully convolutional nature of CNNs the amount of memory required depends on your image. Typical image should run with less than 4 GB of GPU memory consumption. If you run into out of memory problems please check the following: 1) Make sure the voxel spacing of your data is correct and 2) Ensure your MRI image only contains the head region
2. Will you provide the training code as well?
   No. The training code is tightly wound around the data which we cannot make public.
3. What run time can I expect on CPU/GPU?
   This depends on your MRI image size. Typical run times (preprocessing, postprocessing and resampling included) are just a couple of seconds for GPU and about 2 Minutes on CPU (using -tta 0 -mode fast)

¹https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained