Installation

FastSurfer is a pipeline for the segmentation of human brain MRI data. It consists of two main components: the networks for the fast segmentation of an MRI (FastSurferVINN, CerebNet, …) and the recon_surf script for the efficient creation of surfaces and most files and statistics that also FreeSurfer provides.

The preferred way of installing and running FastSurfer is via Singularity or Docker containers on a Linux host system (with a GPU). We provide pre-build images at Dockerhub for various application cases: i) for only the segmentation (both GPU and CPU), ii) for only the CPU-based recon-surf pipeline, and iii) for the full pipeline (GPU or CPU).

We also provide information on a native install on some operating systems, but since dependencies may vary, this can produce results different from our testing environment and we may not be able to support you if things don’t work. Our testing is performed on Ubuntu 22.04 via our provided Docker images.

Linux

Recommended System Spec: 8 GB system memory, NVIDIA GPU with 8 GB graphics memory.

Minimum System Spec: 8 GB system memory (this requires running FastSurfer on the CPU only, which is much slower)

Non-NVIDIA GPU architectures (AMD) are experimental and not officially supported, but seem to work well also.

Singularity

Assuming you have singularity installed already (by a system admin), you can build a Singularity image easily from our Dockerhub images. Run this command from a directory where you want to store singularity images:

singularity build fastsurfer-gpu.sif docker://deepmi/fastsurfer:latest

Additionally, the Singularity README contains detailed directions for building your own Singularity images from Docker.

Example 2 explains how to run FastSurfer (for the full pipeline you will also need a FreeSurfer .license file!) and you can find details on how to build your own images here: Docker and Singularity.

Docker

This is very similar to Singularity. Assuming you have Docker installed (by a system admin) you just need to pull one of our pre-build Docker images from dockerhub:

docker pull deepmi/fastsurfer:latest

Example 1 explains how to run FastSurfer (for the full pipeline you will also need a FreeSurfer .license file!) and you can find details on how to build your own image.

Native (Ubuntu 20.04 or Ubuntu 22.04)

In a native install you need to install all dependencies (distro packages, FreeSurfer in the supported version, python dependencies) yourself. Here we will walk you through what you need.

1. System Packages

You will need a few additional packages that may be missing on your system (for this you need sudo access or ask a system admin):

sudo apt-get update && apt-get install -y --no-install-recommends \
      wget \
      git \
      ca-certificates \
      file

If you are using Ubuntu 20.04, you will need to upgrade to a newer version of libstdc++, as some ‘newer’ python packages need GLIBCXX 3.4.29, which is not distributed with Ubuntu 20.04 by default.

sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
sudo apt install -y g++-11

You also need to have bash-3.2 or higher (check with bash --version).

You also need a working version of python3.10 (we do not support other versions). These packages should be sufficient to install python dependencies and then run the FastSurfer neural network segmentation. If you want to run the full pipeline, you also need a working installation of FreeSurfer (including its dependencies and a license file).

If you are using pip, make sure pip is updated as older versions will fail.

2. Conda for python

We recommend to install conda as your python environment. If you don’t have conda on your system, an admin needs to install it:

wget --no-check-certificate -qO ~/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-py38_4.11.0-Linux-x86_64.sh
chmod +x ~/miniconda.sh
sudo ~/miniconda.sh -b -p /opt/conda && \
rm ~/miniconda.sh 

3. FastSurfer

Get FastSurfer from GitHub. Here you can decide if you want to install the current experimental “dev” version (which can be broken) or the “stable” branch (that has been tested thoroughly):

git clone --branch stable https://github.com/Deep-MI/FastSurfer.git
cd FastSurfer

4. Python environment

Create a new environment and install FastSurfer dependencies:

conda env create -f ./env/fastsurfer.yml 
conda activate fastsurfer

If you do not have an NVIDIA GPU, you can create appropriate ymls on the fly with python ./Docker/install_env.py -m $MODE -i ./env/FastSurfer.yml -o ./fastsurfer_$MODE.yml. Here $MODE can be for example cpu, see also python ./Docker/install_env.py --help for other options like rocm or cuda versions. Finally, replace ./env/fastsurfer.yml with your custom environment file ./fastsurfer_$MODE.yml. If you only want to run the surface pipeline, use ./env/fastsurfer_reconsurf.yml.

Next, add the fastsurfer directory to the python path (make sure you have changed into it already):

export PYTHONPATH="${PYTHONPATH}:$PWD"

This will need to be done every time you want to run FastSurfer, or you need to add this line to your ~/.bashrc if you are using bash, for example:

echo "export PYTHONPATH=\"\${PYTHONPATH}:$PWD\"" >> ~/.bashrc

You can also download all network checkpoint files (this should be done if you are installing for multiple users):

python3 FastSurferCNN/download_checkpoints.py --all

Once all dependencies are installed, you are ready to run the FastSurfer segmentation-only (!!) pipeline by calling ./run_fastsurfer.sh --seg_only .... , see Example 3 for command line flags.

5. FreeSurfer

To run the full pipeline, you will need to install FreeSurfer (we recommend and support version 7.4.1) according to their Instructions. There is a freesurfer email list, if you run into problems during this step.

Make sure, the ${FREESURFER_HOME} environment variable is set, so FastSurfer finds the FreeSurfer binaries.

AMD GPUs (experimental)

We have successfully run the segmentation on an AMD GPU (Radeon Pro W6600) using ROCm. For this to work you need to make sure you are using a supported (or semi-supported) GPU and the correct kernel version. AMD kernel modules need to be installed on the host system according to ROCm installation instructions and additional groups need to be setup and your user added to them, see https://docs.amd.com/bundle/ROCm-Installation-Guide-v5.2.3/page/Introduction_to_AMD_ROCm_Installation_Guide_for_Linux.html .

Build the Docker container with ROCm support.

python Docker/build.py --device rocm --tag my_fastsurfer:rocm

You will need to add a couple of flags to your docker run command for AMD, see Example 1 for **other-docker-flags** or **fastsurfer-flags**:

docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd \
        --device=/dev/dri --group-add video --ipc=host --shm-size 8G \
        **other-docker-flags** my_fastsurfer:rocm \
                **fastsurfer-flags**

Note, that this docker image is experimental, uses a different Python version and python packages, so results can differ from our validation results. Please do visual QC.

MacOS

Processing on Mac CPUs is possible. On Apple Silicon, you can even use the GPU by passing --device mps.

Recommended System Spec: Mac with Apple Silicon M-Chip and 16 GB system memory.

For older Intel CPUs, we only support cpu-only, which will be 2-4 times slower.

Docker (currently only supported for Intel CPUs)

Docker can be used on Intel Macs as it should be similarly fast as a native install there. It would allow you to run the full pipeline.

First, install Docker Desktop for Mac. Start it and set Memory to 15 GB under Preferences -> Resources (or the largest you have, if you are below 15GB, it may fail).

Second, pull one of our Docker containers. Open a terminal window and run:

docker pull deepmi/fastsurfer:latest

Continue with the example in Example 1.

Native

On modern Macs with the Apple Silicon M1 or M2 ARM-based chips, we recommend a native installation as it runs much faster than Docker in our tests. Access to the built-in AI accelerator (MPS) is also only available on native installations. A native installation also works on older Intel chips.

1. Dependency packages

If you do not have git, python3.10 or bash (at least 3.2) you can install these via the packet manager brew. This installs brew and then git and python3.10:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install git python@3.10

2. Python

Create a python environment, activate it, and upgrade pip:

python3.10 -m venv $HOME/python-envs/fastsurfer 
source $HOME/python-envs/fastsurfer/bin/activate
python3.10 -m pip install --upgrade pip

3. FastSurfer and Requirements

Clone FastSurfer:

git clone --branch stable https://github.com/Deep-MI/FastSurfer.git
cd FastSurfer
export PYTHONPATH="${PYTHONPATH}:$PWD"

Install the FastSurfer requirements

python3.10 -m pip install -r requirements.mac.txt

If this step fails, you may need to edit requirements.mac.txt and exclude version numbers that produce conflicts or break our code. On newer M1 Macs, we also had issues with the h5py package, which could be solved by using brew for help (not sure this is needed any longer):

brew install hdf5
export HDF5_DIR="$(brew --prefix hdf5)"
pip3 install --no-binary=h5py h5py

You can also download all network checkpoint files at this point already:

python3.10 FastSurferCNN/download_checkpoints.py --all

Once all dependencies are installed, you can run the FastSurfer segmentation only by calling ./run_fastsurfer.sh --seg_only .... with the appropriate command line flags, see the commandline documentation.

To run the full pipeline, install and source also the supported FreeSurfer version according to their Instructions. There is a freesurfer email list, if you run into problems during this step. Note, that currently FreeSurfer for MacOS supports no ARM, but only Intel, so on modern M-chips it will be slow due to the emulation. This is why we recommend using a Linux host system to run FastSurfer on larger datasets.

4. Apple AI Accelerator support

On modern M-Chips you can try the Apple Silicon AI Accelerator by setting PYTORCH_ENABLE_MPS_FALLBACK and passing --device mps for the segmentation module to make use of the fast GPU:

export PYTORCH_ENABLE_MPS_FALLBACK=1
./run_fastsurfer.sh --seg_only --device mps ....

This will be at least twice as fast as --device cpu. Currently setting the fallback environment variable is necessary as aten::max_unpool2d is not yet implemented for MPS and will fall back to CPU.

Windows

Docker (CPU version)

In order to run FastSurfer on your Windows system using docker make sure that you have:

• WSL2

• Docker Desktop

installed and running.

After everything is installed, start Windows PowerShell and run the following command to pull the CPU Docker image (check on dockerhub what version tag is most recent for cpu):

docker pull deepmi/fastsurfer:cpu-latest

Now you can run Fastsurfer the same way as described in Example 1 for the CPU build, for example:

docker run -v C:/Users/user/my_mri_data:/data \
           -v C:/Users/user/my_fastsurfer_analysis:/output \
           -v C:/Users/user/my_fs_license_dir:/fs_license \
           --rm --user $(id -u):$(id -g) deepmi/fastsurfer:cpu-latest \
           --fs_license /fs_license/license.txt \
           --t1 /data/subjectX/orig.mgz \
           --device cpu \
           --sid subjectX --sd /output \
           --parallel

Note, the system requirements of at least 8GB of RAM for the CPU version. If the process fails, check if your WSL2 distribution has enough memory reserved.

This was tested using Windows 10 Pro version 21H1 and the WSL Ubuntu 20.04 distribution

Docker (GPU version)

In addition to the requirements from the CPU version, you also need to make sure that you have:

• Windows 11 or Windows 10 21H2 or greater,

• the latest WSL Kernel or at least 4.19.121+ (5.10.16.3 or later for better performance and functional fixes),

• an NVIDIA GPU and the latest NVIDIA CUDA driver

• CUDA toolkit installed on WSL, see: CUDA Support for WSL 2

Follow Enable NVIDIA CUDA on WSL to install the correct drivers and software.

After everything is installed, start Windows PowerShell and run the following command to pull the GPU Docker image:

docker pull deepmi/fastsurfer:latest

Now you can run Fastsurfer the same way as described in Example 1, for example:

docker run --gpus all
           -v C:/Users/user/my_mri_data:/data \
           -v C:/Users/user/my_fastsurfer_analysis:/output \
           -v C:/Users/user/my_fs_license_dir:/fs_license \
           --rm --user $(id -u):$(id -g) deepmi/fastsurfer:latest \
           --fs_license /fs_license/license.txt \
           --t1 /data/subjectX/orig.mgz \
           --sid subjectX --sd /output \
           --parallel

Note the system requirements of at least 8 GB system memory and 2 GB graphics memory for the GPU version. If the process fails, check if your WSL2 distribution has enough memory reserved.