Automated 3D nnU-Net Segmentation of the Parasagittal Dura in Children with Autism Spectrum Disorder Using Clinical 3D T2-FLAIR

Background The parasagittal dura (PSD) is a specialized meningeal structure that contains lymphatic vessels and contributes to cerebrospinal fluid (CSF) drainage and neuroimmune surveillance. In adults, aging and neurodegenerative diseases have been associated with increased PSD volume, suggesting that it may represent a potential biomarker of neurodegeneration. However, its functional role and the extent to which PSD volume may reflect pathological processes during early brain development remain poorly understood. Furthermore, no automated tools currently exist to segment the PSD in the very young developing brain. We present a pediatric automated tool for PSD segmentation and investigate PSD volume in children with autism spectrum disorder (ASD) and typically developing (TD) controls. Methods We developed a fully automated segmentation framework based on a 3D nnU-Net applied to clinical 3D T2-FLAIR MRI. The model was trained on manually annotated pediatric ASD scans (n = 144) and evaluated on an independent ASD test set (n = 56). To assess generalizability, the trained model was applied to independent typically developing (TD) cohorts, including an external dataset acquired with different imaging parameters. Total and regional PSD volumes (prefrontal, fronto-parietal, occipital) were quantified and compared between ASD and TD children, and correlations with age and brain volumes were examined. Results The segmentation framework achieved high performance in the ASD cohort (Dice–Sørensen coefficient (DSC) = 0.93 ± 0.02). Comparable performance was observed in the TD cohort (DSC = 0.90 ± 0.02), including the cohort acquired with different imaging parameters (DSC = 0.85 ± 0.04), indicating good generalizability. Performance remained consistent across PSD regions, with slightly higher accuracy in central segments. No significant differences in PSD volumes were found between ASD and TD groups. PSD volume is correlated with CSF (and ea-CSF), supporting a link with fluid compartment dynamics. Conclusions We present a publicly available reliable automated tool for whole-brain PSD segmentation in the pediatric population using clinical 3D T2-FLAIR sequences. Our results show no differences in PSD volumes between ASD and TD groups, while consistently demonstrating a positive correlation with CSF-related compartments. This tool provides a scalable approach for studying the role of PSD in neurofluid circulation during brain development.