ArticleNeuroImage2020
Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 3 of them syntheses that pooled it.
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Who cites it
131 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Presymptomatic Biological, Structural, and Functional Diagnostic Biomarkers of Autism Spectrum Disorder.Journal of neurochemistry · 2025Pooled it
- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological psychiatry · 2023Pooled it
- Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model.PloS one · 2021Trial
- MRI Characterization of Structural Brain Abnormalities in NGLY1 Deficiency.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- Postnatal maturation of putamen microstructure accompanies topographic white matter connectivity and altered circuits in autism.bioRxiv : the preprint server for biology · 2026Article
- Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach.Journal of magnetic resonance imaging : JMRI · 2026Article
- Imaging Biomarkers of the Glymphatic System in the Developing Brain.Korean journal of radiology · 2026Review
- AI segmentation requires accounting for brain size to maintain performance on developmental MRI cohorts.bioRxiv : the preprint server for biology · 2026Article
- Multiscale brain development across the human lifespan.Communications biology · 2026Review
- Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.Brain research bulletin · 2026Article
- Article
- Cortical structure alterations in toddlers with different severities of congenital heart disease: a cross-sectional study.European journal of pediatrics · 2026Article
- Cortical and white matter myelination proceed in concert during early infancy.Nature communications · 2026Article
- miniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants.Human brain mapping · 2026Article
- Bridging the gap: Translating fetal, infant, and toddler neuroimaging insights into clinical practice.Developmental cognitive neuroscience · 2026Review
- A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.PLOS digital health · 2026Article
- BIBSNet: A deep learning baby image brain segmentation network for MRI scans.Developmental cognitive neuroscience · 2026Article
- Developmental human brain connectome from fetal stage to early childhood.Developmental cognitive neuroscience · 2026Review
- Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavior.Nature communications · 2026Article
71 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Registered trials
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.