Home › Research › How common it is
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline
Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M.. NeuroImage, 2020-06-08
Topic: Risk, prevention and care in population studies
Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 min) and surface-based thickness analysis (within only around 1 h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.
Key findings
- To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes.
- The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures.
- Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface.
- This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 min) and surface-based thickness analysis (within only around 1 h runtime).
- For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.
Why it matters
This paper describes a fast deep learning pipeline for automatically processing structural brain MRI scans, replicating FreeSurfer's anatomical segmentation, surface reconstruction, and cortical parcellation. It is a neuroimaging analysis tool and does not test any treatment, cure, or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Alzheimer's in numbers →
Study details
- Study designcohort study
- Evidence strengthModerate
How this study scored 67/100
Published in a peer-reviewed journal, and it is a cohort study following people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A cohort study following people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 6 / 7 | Cited 669 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 83.6 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 5 / 6 | Cited 22.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 4 / 4 | Cited 439 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Martin Reuter: h-index 68 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32526386) |
| Free to read | 2 / 2 | Free to read |
| Total | 67 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2020.117012
- PubMed: 32526386
- Original source →
- Free full text: NeuroImage →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B., & Reuter, M. (2020). FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.117012
Vancouver
Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. NeuroImage. 2020. doi:10.1016/j.neuroimage.2020.117012. PMID: 32526386.
BibTeX
@article{henschel2020-32526386,
author = {Henschel, L. and Conjeti, S. and Estrada, S. and Diers, K. and Fischl, B. and Reuter, M.},
title = {{FastSurfer - A fast and accurate deep learning based neuroimaging pipeline}},
journal = {NeuroImage},
year = {2020},
doi = {10.1016/j.neuroimage.2020.117012},
pmid = {32526386},
url = {https://doi.org/10.1016/j.neuroimage.2020.117012}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
4 studies in the alzhub library list this one in their references.
Related studies in How common it is
Also tagged “cerebral”
900 studies in the library carry the topic tag “cerebral”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 900 tagged studies.