FastSurfer is a fast and accurate deep-learning pipeline for the analysis of human brain MRI. FastSurfer provides a fully compatible FreeSurfer alternative for volumetric and surface-based thickness analysis, also supporting sub-mm resolutions, and sub-segmentation of neuroanatomical structures.
FastSurfer is a fast and accurate deep-learning based neuroimaging pipeline for the automated processing of structural human brain MRIs. Based on a T1-weighted MRI you get everything you need for quick structure localization (whole brain segmentation in under 1 minute), extraction of quantitative measures, group analysis of your cohort, or structural pre-processing for fMRI/diffusion analysis:
Beyond the speed-up compared to traditional tools, FastSurfer and its components are highly accurate, reliable and sensitive to group effects. To ensure usefulness, we extensively validated the entire pipeline using a number of publicly available datasets (see Proof of Concept).
Further, running FastSurfer is easy as pie - a single command does all the work for you. The full documentation can be found on our github page.
./run_fastsurfer.sh \ --sd /home/user/my_fastsurfer_analysis \ # output dir (=SUBJECTS_DIR in FreeSurfer) --sid SUBJECT_ID \ # result folder (auto-generated in output dir) --t1 /home/user/my_mri_data/T1.nii.gz # Location of your T1-weighted MRI
We also provide Docker and Singularity container with all dependencies installed in it. You only need a valid FreeSurfer license for the surface stream, which is normally called .license or licence.txt and can be found in your FREESURFER_HOME directory or you can download it for free at https://surfer.nmr.mgh.harvard.edu/registration.html.
docker run --gpus all \ -v /home/user/my_mri_data:/DATA_INPUT_DIRECTORY \ -v /home/user/my_fastsurfer_analysis:/DATA_OUTPUT_DIRECTORY \ -v /home/user/my_fs_license_dir:/fs_license \ --rm --user $(id -u):$(id -g) \ deepmi/fastsurfer:latest --sd DATA_OUTPUT_DIRECTORY \ --sid SUBJECT_ID \ --t1 DATA_INPUT_DIRECTORY/T1.nii.gz \ --fs_license /fs_license/.license \
Answer: Very good! FastSurfer outperforms other deep-learning architectures by a significant margin both with respect to FreeSurfer and a manual standard (Mindboggle). It generalizes well across five different datasets including subjects with different disease states (e.g. cognitive normal, mild cognitive impaired or demented subjects in OASIS, ADNI, MIRIAD), different vendors (e.g. THP), age groups, downsampled and defaced images (e.g. HCP) and alternative T1-imaging protocols (e.g. MIRIAD)
Answer: FastSurfer is highly reliable as demonstrated by the close agreement between the thickness and volumetric measurements for 20 Test-Retest subjects from OASIS1. FastSurfer exhibits high test-retest reliability and improves agreement between the cortical thickness measurements compared to FreeSurfer.
Answer: FastSurfer is capable of accurately reproducing known disease effects in a control versus dementia cross-sectional group study. Reduced cortical thickness in regions associated with dementia (e.g. temporal lobes) as well as subcortical volume differences (e.g. enlarged ventricles, shrunken Hippocampus) are robustly detected with increased sensitivity relative to FreeSurfer.
FastSurfer is available as open source at github.
An accompanying set of quality control tools can be found at github.
Or watch our small presentation (10min) on YouTube.