Year: 2026
Type: preprint
Abstract: Quantitative assessment of head growth supports early detection of neurological disease, yet current clinical practice relies on manual measurements that are variable, anatomically limited, and difficult to scale. Here we introduce a fully automated imaging-native framework (CranioTrace) that transforms routine neuroimaging into standardized, population-referenced markers of cranial development. By integrating topology-constrained segmentation, contour regularization, and geometry-based quality control for automated slice selection, CranioTrace robustly extracts head circumference and cranial morphology from heterogeneous MRI and CT data without manual intervention. The framework enabled construction of sex-specific population growth models across pediatric development in a dataset comprising 9,685 scans spanning birth to early adulthood across 25 cohorts. Imaging-derived head circumference shows strong agreement with tape measurements across independent datasets, high reproducibility (intraclass correlation up to 0.99), and consistent performance across modalities. Beyond conventional head circumference, CranioTrace quantifies cranial shape and asymmetry, capturing developmental dynamics that are not accessible through conventional measurements. Population modeling reveals rapid early-life expansion followed by nonlinear deceleration and stable sex differences. Application to neurogenetic cohorts identifies disease-consistent shifts in growth trajectories: head circumference was higher in neurofibromatosis type 1 and 16p11.2 deletion and lower in 22q11.2 deletion and 16p11.2 duplication. By converting clinical imaging archives into scalable cranial phenotypes linked to probabilistic reference models, CranioTrace provides a foundation for imaging-based growth charting, integrated skull-brain phenotyping, and precise assessment of neurodevelopment.
Authors: P. Mattisson, A.S. Mandal, M. Gardner, A. Zapaishchykova, B. Jung, S. Prem, S. Karandikar, H.E. Akouri, D. Zimmerman, E. Levitis, J.A. Berken, G. Ball, R.A.I. Bethlehem, J.D. Bernstock, T.P. Roberts, L. Bloy, H. Huang, A. Vossough, S. Sotardi, E. Liao, M. Fisher, D.M. McDonald-McGinn, R.T. Shinohara, B.H. Kann, A.F. Alexander-Bloch, J. Seidlitz
Institutions: Children's Hospital of Philadelphia, University of Pennsylvania Health System, University of Pennsylvania, Harvard University, Mass General Brigham, Brigham and Women's Hospital, Boston Children's Hospital, Dana-Farber Cancer Institute, Dana-Farber Brigham Cancer Center, The University of Melbourne, Murdoch Children's Research Institute, University of Cambridge, Baylor College of Medicine, Texas Children's Hospital
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: An imaging-genomic approach to advance understanding of early cortical development and outcome after preterm birth, Radiomics for Clinically-Acquired Brain MRIs of Youth with Neurodevelopmental Disorders, Precision brain charts for imaging-genomics of schizophrenia and the psychosis spectrum
OpenAlex ID: W7207813495