Year: 2026
Type: preprint
Abstract: Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in clinical scans. In particular, many tools underperform in infant scans due to morphological and physical changes such as rapid myelination. Here, we introduce ClinSeg: a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast. We leverage existing registration and synthetic data generation tools to construct a training corpus for a 3d U-Net spanning anatomical and contrast diversity, including scans with morphological abnormalities from a pediatric hospital. Validated against manual segmentations, ClinSeg outperforms existing models in infancy while matching them in childhood and adolescence. Finally, ClinSeg enables the construction of reference brain growth trajectories in 11,699 individuals from 0-21 years of age, leading to the detection of more nuanced age-related findings in clinical groups.
Source: medRxiv
Authors: Elizabeth Levitis, Henry F. J. Tregidgo, Dabriel Zimmerman, Benjamin Jung, Shivaram Karandikar, Margaret Gardner, Philip Mattisson, Eren Kafadar, Anna Zapaishchykova, Benjamin H. Kann, Susan T. Sotardi, Arastoo Vossough, Hao Huang, Benjamin Billot, Juan Eugenio Iglesias, Daniel C. Alexander, Aaron Alexander-Bloch, Jakob Seidlitz
Institutions: Children's Hospital of Philadelphia, University of Pennsylvania Health System, University of Pennsylvania, University College London, Brigham and Women's Hospital, Boston Children's Hospital, Harvard University, Dana-Farber Cancer Institute, Dana-Farber Brigham Cancer Center, Mass General Brigham, Institut national de recherche en sciences et technologies du numérique, Université Côte d'Azur, EPIONE: E-Patient : Images, données & mOdèles pour la médeciNe numériquE, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7206188849