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 (or Apptainer)

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 documentation contains detailed directions for building your own Singularity images from Docker.

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 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 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 image.

If you are using the rootless mode, you have to install the NVIDIA Container Toolkit and follow the configuration for the rootless mode. Otherwise, running FastSurfer with Docker will give you this error message docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

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 (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. uv for python

We recommend to install uv as your python environment and package manager. uv is a very fast package manager, which makes managing different environments even easier. See uv’s documentation for more information on installation such as autocompletion info.

wget -qO- https://astral.sh/uv/install.sh | 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):

cd /path/to/install
# FastSurfer will get cloned to /path/to/install/FastSurfer
git clone --branch stable https://github.com/Deep-MI/FastSurfer.git
cd FastSurfer

4. Python environment

Create a new environment and install FastSurfer dependencies:

# make sure you are in the FastSurfer directory!
# create a .venv environment directory inside /path/to/install/FastSurfer with the FastSurfer dependencies
# the minimum required python version is 3.10
uv venv --python python3.12
# install packages with pinned versions from the last stable release (recommended)
uv pip sync requirements.txt

uv will also try to find the correct backend for your hardware, but you can manually specify the backend for testing purposes:

# make sure you are in the FastSurfer directory!
uv pip sync requirements.txt --torch-backend cpu

For developers: To install the latest compatible dependency versions instead of the pinned stable ones, replace requirements.txt with pyproject.toml in the commands above:

uv pip sync pyproject.toml

You can now activate the FastSurfer environment with

source .venv/bin/activate

Next, add the fastsurfer directory to the python path:

# make sure you are in the FastSurfer directory!
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:

# make sure you are in the FastSurfer directory!
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):

# make sure you are in the FastSurfer directory!
python 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://rocm.docs.amd.com/projects/install-on-linux/en/latest/ .

Build the Docker container with ROCm support.

python tools/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 2 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 2.

Package

1. Requirements

An Apple silicon Mac (M1/M2/M3/…). Intel Macs cannot use the package: PyTorch has published no macOS x86_64 wheels since 2.2, so the environment the package bundles cannot be built for them. On an Intel Mac, use Docker above instead, which runs natively there and supports the full pipeline.

macOS 14 (Sonoma) or newer: the bundled binaries are compiled for it and cannot load on anything older. We do not test specific versions, so treat this as a lower bound rather than a support statement.

Only the shells macOS already ships: /bin/bash (at least 3.2) for FastSurfer’s own scripts and /bin/tcsh for the FreeSurfer ones. Your own Terminal shell does not matter – the applet starts a bash session itself – though setting the environment up by hand (see below) needs bash or zsh. No Python installation and no Homebrew are needed: the package bundles its own Python, all Python dependencies, the network checkpoints and a reduced FreeSurfer, so installing it requires no internet connection and downloads nothing.

On Apple silicon, the full pipeline additionally needs Rosetta 2. FastSurfer bundles the official FreeSurfer build, which upstream currently ships for Intel only, so the FreeSurfer executables run through Rosetta even in the Apple silicon package. Segmentation (--seg_only) does not use them and works without it. Most Macs already have Rosetta 2, and if not, you can install it once with:

softwareupdate --install-rosetta

Note this is the one step that does need an internet connection, so install it before you rely on the package being fully offline.

In exchange the installer is large: expect a download of under a gigabyte and a couple of gigabytes of disk space once installed.

2. FastSurfer package

Download FastSurfer-macos-darwin_arm64.pkg, which always points at the newest release. Its version is shown in the installer window, and after installing by run_fastsurfer.sh --version. Earlier versions are on the releases page.

To install, double-click the downloaded .pkg installer and follow the installer instructions.

Note: FastSurfer’s .pkg is currently not signed/notarized by Apple, so macOS Gatekeeper will block it. Depending on your macOS version, double-clicking the installer may not show an “Open” option at all, just “Done” or “Move to Trash”, in which case nothing happens if you click “Done”. To allow it: click Done on that warning, then go to System Settings > Privacy & Security, scroll down to the Security section, and click Open Anyway next to the message about the blocked installer. Confirm once more (you may be asked for your password or Touch ID), then double-click the .pkg again to start the installation.

After installation, you can find the FastSurfer applet, its source code, and selected FreeSurfer executables in the /Applications folder.

3. Launching FastSurfer

To launch a configured FastSurfer terminal session, start the FastSurfer applet from Applications. This opens a regular Terminal window running a FastSurfer console: a bash session with everything already set up to run FastSurfer, recognizable by the (FastSurfer<version>) prompt prefix. It:

• puts the Python distribution bundled with FastSurfer (FASTSURFER_HOME/python) first on PATH,

• sets FASTSURFER_HOME and PYTHONPATH,

• sets FREESURFER_HOME to the pruned FreeSurfer installation bundled with FastSurfer and sources SetUpFreeSurfer.sh,

• adds the FastSurfer directory (and GNU grep, if you happen to have it via Homebrew) to your PATH, for this session only – no shell profile is modified,

• reads your ~/.bashrc first, if you have one, so your own aliases and settings are still there, and

• reminds you to set FS_LICENSE if it is not already set (see “FreeSurfer license” below).

In this console, you can run the full FastSurfer pipeline by typing and executing run_fastsurfer.sh <fastsurfer-flags>, where you replace <fastsurfer-flags> with the appropriate commandline flags of FastSurfer, for example:

run_fastsurfer.sh --seg_only --sd <path/to/output/dir> --sid <subject_id> --t1 <path/to/subjects/t1/image>

or, for the full pipeline:

run_fastsurfer.sh --sd <path/to/output/dir> --sid <subject_id> --t1 <path/to/subjects/t1/image> --fs_license ~/fs_license.txt

No --device flag is needed: the default already picks the Apple GPU (mps) where it is available and the CPU otherwise. Passing --device mps explicitly is an error on a Mac without an MPS-capable GPU, rather than falling back.

You do not need to use the applet: in a bash or zsh Terminal window, you can set up exactly the same environment by sourcing the same script:

source /Applications/FastSurfer<version>/macos_setup_fastsurfer.sh

The script is bash syntax, so from a shell that does not understand it, such as tcsh or fish, start a bash (or zsh) session first and source it in there.

Adding only the FastSurfer directory to your PATH instead is not enough and is best avoided: run_fastsurfer.sh would be found, but python3 would still be Apple’s system Python – too old for FastSurfer – and FREESURFER_HOME would be unset, so it fails with a confusing error. Sourcing the script sets all of it.

4. FreeSurfer license (for surfaces / eTIV)

A FreeSurfer license is only needed if you run the surface module (recon-surf) or, in segmentation-only mode, activate the Talairach registration via --tal_reg (used to estimate total intracranial volume, eTIV, in the stats files). Plain segmentation without --tal_reg does not need one.

To get a license, register at the FreeSurfer website to acquire a FreeSurfer license (for free).

Unlike a native Linux/source install, do not rely on FastSurfer auto-detecting the license inside $FREESURFER_HOME: on macOS, $FREESURFER_HOME points at the pruned FreeSurfer bundled with the package ($FASTSURFER_HOME/fs-pruned), which is installed by the .pkg as root and is not writable by your user account. Instead, save the license file somewhere in your home directory and either pass it explicitly:

run_fastsurfer.sh ... --fs_license ~/fs_license.txt

or export it once per console session:

export FS_LICENSE=~/fs_license.txt

or, to have it set in every shell, add that line to your shell profile yourself (~/.zprofile for zsh, the macOS default; ~/.bash_profile for bash). FastSurfer does not modify these files.

5. Apple AI Accelerator support

On modern M-Chips you can try the Apple Silicon AI Accelerator by passing --device mps for the segmentation module to make use of the fast GPU (when using run_fastsurfer.sh, FastSurfer sets PYTORCH_ENABLE_MPS_FALLBACK=1 automatically on macOS unless you already set it):

./run_fastsurfer.sh --seg_only --device mps ....

This will be at least twice as fast as --device cpu. The fallback is needed because aten::max_unpool2d is not yet implemented for MPS; expect a one-time warning about it, which is harmless.

6. Uninstalling

Drag both items from your Applications folder to the Trash:

• FastSurfer<version> (the installation)

• FastSurfer<version>.app (the applet)

macOS will ask for your password, because the installer places them as root. Everything FastSurfer installed lives in that one directory: no shell profile is modified and nothing is written elsewhere, so there is nothing else to clean up. Installations of other versions are independent and are not affected.

Optionally, to also drop the installer’s receipt (bookkeeping only, it does not affect anything you run):

sudo pkgutil --forget org.deep-mi.FastSurfer.<version-without-dots>_<arch>

pkgutil --pkgs | grep -i fastsurfer lists the exact identifiers.

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 2 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

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 2, 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

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.