Introduction to FastSurfer¶
We are excited that you are here. In this documentation we will help you get started with FastSurfer!
FastSurfer is an open-source AI software tool to extract quantiative measurements from human brain MRI (T1-weighted) images. You will learn about it’s different segmentation and surface modules and how to install and run it natively or in the recommended Docker or Singularity images. But first let us tell you why we think FastSurfer is great:
FastSurfer uses dedicated and fast AI methods (developed in-house).
It is thoroughly validated across different scanners, field-strenghts, T1 sequences, ages, diesease, …
FastSurfer is fully open-source using a permissive Apache license.
It is compatible with FreeSurfer, enabling FreeSurfer downstream tools to work directly.
It is much faster and provides increased reliability and sensitivity of the derived measures.
It natively supports high-resolution images (down to around 0.7mm) at high accuracy.
It has modules for full-brain (aseg+aparcDKT), cerebellum and hypothalamic sub-segmentations.
The segmentation modules run within minutes and provide partial-volume corrected stats.
It has an optimized surface stream for cortical thickness analysis and improved correspondence.
So, there is really no reason why you should not try this out!
System Requirements¶
Recommendation: At least 8 GB system memory and 8 GB NVIDIA graphics memory
Minimum Requirements:¶
–viewagg_device |
Min GPU (in GB) |
Min CPU (in GB) |
|
|---|---|---|---|
1mm |
gpu |
5 |
5 |
1mm |
cpu |
2 |
7 |
0.7mm |
gpu |
8 |
6 |
0.7mm |
cpu |
3 |
9 |
0.7mm |
–device cpu |
0 |
9 |
The default device is the GPU. The view-aggregation device can be switched to CPU and requires less GPU memory. CPU-only processing --device cpu is much slower and not recommended.
Expert usage¶
Individual modules and the surface pipeline can be run independently of the full pipeline script documented in this documentation. This is documented in READMEs in subfolders, for example: whole brain segmentation only with FastSurferVINN, cerebellum sub-segmentation, hypothalamic sub-segmentation and surface pipeline only (recon-surf).
Specifically, the segmentation modules feature options for optimized parallelization of batch processing.
FreeSurfer Downstream Modules¶
FreeSurfer provides several Add-on modules for downstream processing, such as subfield segmentation ( hippocampus/amygdala, brainstem, thalamus and hypothalamus ) as well as TRACULA. We now provide symlinks to the required files, as FastSurfer creates them with a different name (e.g. using “mapped” or “DKT” to make clear that these file are from our segmentation using the DKT Atlas protocol, and mapped to the surface). Most subfield segmentations require wmparc.mgz and work very well with FastSurfer, so feel free to run those pipelines after FastSurfer. TRACULA requires aparc+aseg.mgz which we now link, but have not tested if it works, given that DKT-atlas merged a few labels. You should source FreeSurfer 7.3.2 to run these modules.
Intended Use¶
This software can be used to compute statistics from an MR image for research purposes. Estimates can be used to aggregate population data, compare groups etc. The data should not be used for clinical decision support in individual cases and, therefore, does not benefit the individual patient. Be aware that for a single image, produced results may be unreliable (e.g. due to head motion, imaging artefacts, processing errors etc). We always recommend to perform visual quality checks on your data, as also your MR-sequence may differ from the ones that we tested. No contributor shall be liable to any damages, see also our software LICENSE.
References¶
If you use this for research publications, please cite:
Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M, FastSurfer - A fast and accurate deep learning based neuroimaging pipeline, NeuroImage 219 (2020), 117012. https://doi.org/10.1016/j.neuroimage.2020.117012
Henschel L*, Kuegler D*, Reuter M. (*co-first). FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods - A Solution for HighRes Brain MRI. NeuroImage 251 (2022), 118933. http://dx.doi.org/10.1016/j.neuroimage.2022.118933
Faber J*, Kuegler D*, Bahrami E*, et al. (*co-first). CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. NeuroImage 264 (2022), 119703. https://doi.org/10.1016/j.neuroimage.2022.119703
Estrada S, Kuegler D, Bahrami E, Xu P, Mousa D, Breteler MMB, Aziz NA, Reuter M. FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI. Imaging Neuroscience 2023; 1 1–32. https://doi.org/10.1162/imag_a_00034
Stay tuned for updates and follow us on X/Twitter.
Acknowledgements¶
This project is partially funded by:
The recon-surf pipeline is largely based on FreeSurfer.