# FreeSurfer Development, Maintenance, and Hardening

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $575,580

## Abstract

Abstract:
Imaging of the human brain has seen explosive growth in the last two decades mainly through the various
modalities of MRI. The massive amount of data requires automatic and robust tools for analysis. FreeSurfer
(FS, surfer.nmr.mgh.harvard.edu) is one of the preeminent tools used for neuroimage analysis. FS has more
than 44,000 downloads, and the core FS manuscripts have been cited more than 22,000 times. FS is part of the
analysis core for many NIH-funded large-scale data acquisition projects such as the Human Connectome Project
(HCP), Alzheimer's Disease Neuroimaging Initiative (ADNI), Framingham Heart Study (FHS), The Adolescent
Brain Cognitive Development (ABCD), as well as the UK BioBank. One third of the 600+ ADNI-based publications
cite FS. Simply put, much of the innovative research done in neuroimaging would not be possible without FS.
Started in 1998, FS is best known for providing detailed and automated anatomical analysis of T1-weighted MRI
images, especially for the cortical surface. However, FS anatomical analysis provides an ideal substrate for all
modes of brain imaging including functional MRI, diffusion MRI, PET, optical/NIRS, as well as EEG/MEG. FS
provides tools to perform these analyses as well as software to integrate with other analysis tools (e.g., SPM,
FSL, AFNI). FS has been used for presurgical planning and even in the operating room.
 The original grant mostly centered around Sequence Adaptive Multimodal Segmentation (SAMSEG).
SAMSEG uses parametric Bayesian generative modeling to segment brain images. The SAMSEG framework
fits atlas priors and multivariate Gaussian intensity models to brain images (including MRI artifacts such as bias
fields). SAMSEG can take any modality or combination of modalities as input. Since it adapts its intensity model,
it is robust to differences in scanner. Since it is a generative model, it is easy to extend to encompass more
segmentation details. For example, the SAMSEG framework has been used to segment hippocampal subfield,
amygdalar nuclei, thalamic nuclei, and extracerebral structures.
 The main vision for the renewal is to extend the SAMSEG framework to accommodate longitudinal
models, incorporate more anatomical details, and to use SAMSEG output as a basis for cortical surface
placement that is, like SAMSEG, modality independent and capable of using any combination of modalities. In
addition, we propose a series of new tools that will assist in the individual and group analysis of large studies by
creating study-specific models. In addition to this new technical development, we are requesting support for
software engineering, maintenance, and user support – mundane and not innovative, but high-impact this type
of support is critical to the thousands of researchers who rely on FreeSurfer.

## Key facts

- **NIH application ID:** 10326397
- **Project number:** 5R01EB023281-06
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Bruce Fischl
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $575,580
- **Award type:** 5
- **Project period:** 2016-09-15 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10326397

## Citation

> US National Institutes of Health, RePORTER application 10326397, FreeSurfer Development, Maintenance, and Hardening (5R01EB023281-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10326397. Licensed CC0.

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