Automatic brain quantification in children with unilateral cerebral palsy

Abstract

Assessing brain damage in children with spastic unilateral cerebral palsy (uCP) is challenging, particularly in clinical settings. In this study, we developed and validated a deep learning-based pipeline to automatically quantify lesion-free brain volumes. Using T1-weighted and FLAIR MRI data from 35 patients (aged 5-15 years), we trained models to segment brain structures and lesions, utilizing an automatic label generation workflow. Validation was performed on 54 children with CP (aged 7-16 years) using quantitative and qualitative metrics, as well as an independent dataset of 36 children with congenital or acquired brain anatomy distortions (aged 1-17 years). Clinical evaluation examined the correlation of lesion-free volumes with visual-based assessments of lesion extent and motor and visual outcomes. The models achieved robust segmentation performance in brains with severe anatomical alterations and heterogeneous lesion appearances, identifying reduced volumes in the affected hemisphere, which correlated with lesion extent (p < 0.05). Further, regional lesion-free volumes, especially in subcortical structures such as the thalamus, were linked to motor and visual outcomes (p < 0.05). These results support the utility of automated lesion-free volume quantification for exploring brain structure-function relationships in uCP.