Evidence map›Paper›PMID 42125201›Full record

ArticleImaging neuroscience (Cambridge, Mass.)

FastSurfer-CC: A robust, accurate, and comprehensive framework for corpus callosum morphometry.

Clemens Pollak, Kersten Diers, Santiago Estrada, David Kügler, Martin Reuter

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Clemens PollakAI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.ORCID https://orcid.org/0009-0001-1645-7609
Kersten DiersAI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Santiago EstradaAI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
David KüglerAI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Martin ReuterAI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.

Funding

Deep Learning Algorithms for FreeSurferR01AG064027 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2020 to 2024
$3.4M
FastPlex: A Fast Deep Learning Segmentation Method for Accurate Choroid Plexus MorphometryR01MH131586 · NIMH · UNIVERSITY OF ROCHESTER · PI Paulo L Lizano · 2023 to 2026
$2.3M
NIA NIH HHS R01 AG064027NIMH NIH HHS R01 MH131586
6 · The paper itself

Abstract

The corpus callosum, the largest commissural structure in the human brain, is a central focus in research on aging and neurological diseases. It is also a critical target for interventions such as deep brain stimulation and serves as an important biomarker in clinical trials, including those investigating remyelination therapies. Despite extensive research on corpus callosum segmentation, few publicly available tools provide a comprehensive and automated analysis pipeline. To address this gap, we present FastSurfer-CC, an efficient and fully automated framework for corpus callosum morphometry. FastSurfer-CC automatically identifies mid-sagittal slices, segments the corpus callosum and fornix, localizes the anterior and posterior commissures to standardize head positioning, generates thickness profiles and subdivisions, and extracts eight shape metrics for statistical analysis. We demonstrate that FastSurfer-CC outperforms existing specialized tools across the individual tasks. Moreover, our method reveals statistically significant differences between Huntington's disease patients and healthy controls that are not detected by the current state-of-the-art.

Indexed as

commissurecorpus callosumdeep learninghead poselocalizationsegmentation

Identifiers

PMID42125201
PMCPMC13159026

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.