Evidence map›Paper›PMID 42013743›Full record

ArticleDevelopmental cognitive neuroscience2026

BIBSNet: A deep learning baby image brain segmentation network for MRI scans.

Timothy J Hendrickson, Paul Reiners, Lucille A Moore, Jacob T Lundquist, Begim Fayzullobekova, Anders J Perrone, Erik G Lee, Julia Moser, Trevor K M Day, Dimitrios Alexopoulos and 20 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Developmental cognitive neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026
    Review
  8. Article
  9. Article
  10. Precision functional imaging in infants using multi-echo fMRI at 7T.bioRxiv : the preprint server for biology · 2025
    Article
  11. Deep infant brain segmentation from multi-contrast MRI.Conference record. Asilomar Conference on Signals, Systems & Computers · 2025
    Article
  12. Article
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  15. MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025
    Article
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  18. The American journal of drug and alcohol abuse · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

30 authors.

Timothy J HendricksonMinnesota Supercomputing Institute, University of Minnesota, USA; Masonic Institute for the Developing Brain, University of Minnesota, USA. Electronic address: hendr522@umn.edu.
Paul ReinersMasonic Institute for the Developing Brain, University of Minnesota, USA.
Lucille A MooreMasonic Institute for the Developing Brain, University of Minnesota, USA.
Jacob T LundquistMasonic Institute for the Developing Brain, University of Minnesota, USA.
Begim FayzullobekovaMasonic Institute for the Developing Brain, University of Minnesota, USA.
Anders J PerroneMasonic Institute for the Developing Brain, University of Minnesota, USA.
Erik G LeeMinnesota Supercomputing Institute, University of Minnesota, USA; Masonic Institute for the Developing Brain, University of Minnesota, USA.
Julia MoserMasonic Institute for the Developing Brain, University of Minnesota, USA.
Trevor K M DayMasonic Institute for the Developing Brain, University of Minnesota, USA; Institute of Child Development, University of Minnesota, USA; Center for Brain Plasticity and Recovery, Georgetown University, USA.
Dimitrios AlexopoulosDepartments of Neurology, Pediatrics, Radiology, and Psychiatry, Washington University in St. Louis, USA.
Martin StynerDepartment of Psychiatry, University of North Carolina at Chapel Hill, USA.
Omid KardanDepartment of Psychology, University of Chicago, USA; University of Michigan, USA.
Taylor A ChamberlainDepartment of Psychology, University of Chicago, USA.
Anurima MummaneniDepartment of Psychology, University of Chicago, USA.
Henrique A CaldasDepartment of Psychology, University of Chicago, USA.
Brad BowerPrimeNeuro, USA.
Sally StoyellMasonic Institute for the Developing Brain, University of Minnesota, USA.
Tabitha MartinMasonic Institute for the Developing Brain, University of Minnesota, USA.
Sooyeon SungMasonic Institute for the Developing Brain, University of Minnesota, USA.
Ermias A FairMasonic Institute for the Developing Brain, University of Minnesota, USA.
Kenevan CarterMasonic Institute for the Developing Brain, University of Minnesota, USA.
Jonathan Uriarte-LopezOregon Health & Science University, USA. Electronic address: jonathanuriartelopez@gmail.com.
Amanda R RueterDepartment of Neurology, University of Minnesota, USA.
Essa YacoubDepartment of Radiology, University of Minnesota, USA; Center for Magnetic Resonance Research, University of Minnesota, USA.
Monica D RosenbergDepartment of Psychology, University of Chicago, USA.
Christopher D SmyserDepartments of Neurology, Pediatrics, Radiology, and Psychiatry, Washington University in St. Louis, USA.
Jed T ElisonMasonic Institute for the Developing Brain, University of Minnesota, USA; Institute of Child Development, University of Minnesota, USA; Department of Pediatrics, University of Minnesota, USA.
Alice GrahamOregon Health & Science University, USA.
Damien A FairMasonic Institute for the Developing Brain, University of Minnesota, USA; Institute of Child Development, University of Minnesota, USA; Department of Pediatrics, University of Minnesota, USA.
Eric FeczkoMasonic Institute for the Developing Brain, University of Minnesota, USA; Department of Pediatrics, University of Minnesota, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesBrain tissue segmentation of infant magnetic resonance (MR) images is important for studying typical and atypical brain development. The infant brain undergoes rapid changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. We introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source model for robust and generalizable brain tissue segmentation leveraging data augmentation and a large sample size of manually annotated images. EXPERIMENTAL

designModel training included MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance was assessed by comparing BIBSNet, and joint label fusion (JLF) inferred segmentations to ground truth segmentations, and an ad-hoc analysis with iBeat inferred segmentation, using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to produce anatomical and resting state functional derivatives to further assess model performance on processed derivatives. PRINCIPAL OBSERVATIONS: BIBSNet outperforms JLF based on DSC comparisons especially with gray matter (BIBSNet = 0.849, JLF = 0.713) and white matter (BIBSNet = 0.862, JLF = 0.791). Additionally, with processed derived metrics, BIBSNet inferred segmentations outperforms JLF inferred segmentations across nearly all anatomical and functional metrics. Ad-hoc analyses of cortical segmentations - iBeat does not perform subcortical segmentations - showed that there is no significant difference between iBeat and BIBSNet segmentation for infants 0-5 months, but iBeat performed significantly better for infants 6-8 months.

conclusionsBIBSNet shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF at segmentation inference, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

Indexed as

BrainDeep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingFemaleHumansInfantInfant, NewbornMaleAnatomical Brain Tissue SegmentationDeep LearningInfancyMRIProcessing Methods

Identifiers

PMID42013743
PMCPMC13122235

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.