E. Levitis, H. F. J. Tregidgo, D. Zimmerman, B. Jung, S. Karandikar, M. Gardner, P. Mattisson, E. Kafadar, A. Zapaishchykova, B. H. Kann, S. T. Sotardi, A. Vossough, H. Huang, B. Billot, J. E. Iglesias Gonzales, D. C. Alexander, A. F. Alexander-Bloch, J. Seidlitz
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.