Baby Open Brains: An open-source dataset of infant brain segmentations

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold-standard brain segmentations. These segmentations are limited by the difficulty of infant brain segmentations, which require extensive neuroanatomical knowledge and are time-consuming in nature. Addressing this, we constructed the Baby Open Brains (BOBs) Dataset, an open source resource of manually curated and expert reviewed infant brain segmentations. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1-9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

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Publication Details

Journal
Scientific Data
Published
2025-08-14
DOI
https://doi.org/10.1038/s41597-025-05404-y
Citations
2
Primary Topic
Neonatal and fetal brain pathology
Type
article
Field-Weighted Citation Impact
6.99

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article

Baby Open Brains: An open-source dataset of infant brain segmentations

Elizabeth A. Kiffmeyer, Timothy Hendrickson, Jed T. Elison, Audrey Houghton et al.
2 citations
Scientific Data
Neonatal and fetal brain pathology
6.99
article

Baby Open Brains: An open-source dataset of infant brain segmentations

Elizabeth A. Kiffmeyer, Timothy Hendrickson, Jed T. Elison, Audrey Houghton, Tabitha Martin, Eric Feczko, Sooyeon Sung, Lucille A. Moore, Monica D. Rosenberg, Essa Yacoub, Amanda Rueter, Trevor K. M. Day, Anurima Mummaneni, Damien A. Fair, Christopher D. Smyser, Greg Conan, Sally M. Stoyell, Paul Reiners, Martin Andreas Styner, Taylor A. Chamberlain, Brad Bower, Anders Perrone, Erik Lee, Jessica L. Wisnowski, Maria Bagonis, Jacob T. Lundquist, Omid Kardan, Alice Graham, Dimitrios Alexopoulos, Dhruman Goradia, Lucas Heisler-Roman, Carina Lucena, Mollie Myricks, Pranav Narnur, Hteemoo Saw, Barry Tiklasky, Kenneth Barrett, Addison Cavender, Brett Zimmermann
article en
2 citations

Abstract

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold-standard brain segmentations. These segmentations are limited by the difficulty of infant brain segmentations, which require extensive neuroanatomical knowledge and are time-consuming in nature. Addressing this, we constructed the Baby Open Brains (BOBs) Dataset, an open source resource of manually curated and expert reviewed infant brain segmentations. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1-9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Scientific DataVol. 12(1)
University of North Carolina at Chapel Hill (US), University of Southern California (US), University of Minnesota (US), Oregon Health & Science University (US), Washington University in St. Louis (US), University of Michigan (US), University of Chicago (US), Resonance Research (United States) (US), Public Risk Management Association (US)
National Science Foundation, Bill and Melinda Gates Foundation, National Institute of Mental Health, National Institute on Drug Abuse
Openalex Percentile: Top 4%
Neonatal and fetal brain pathology
6.99
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