Improved BG-PVS Quantification in Infant Brain MRI Using Anatomy-Informed Pseudo-Labels for Joint BG and PVS Segmentation

Abstract

Background Reliable quantification of perivascular spaces (PVS) in the basal ganglia (BG) is of growing interest for understanding the glymphatic system but remains challenging in infants. Purpose To develop an automated deep learning method for BG and BG-PVS segmentation in infant brain MRI using an anatomy-informed pseudo-labeling approach.Study Type Retrospective, multi-cohort technical development, and validation study. Population Three cohorts: 150 neonates from the Developing Human Connectome Project (dHCP, 37-44 weeks of gestational age (GA); 76 males, 74 females), 133 infants from the Baby Connectome Project (BCP; <= 24 months; 70 males, 63 females) and 70 infants from an in-house dataset (30-41 weeks of GA; 36 males, 34 females). Manual ground-truth labels were generated by a trained researcher (dHCP, n = 150; BCP, n = 8; in-house, n = 10) and validated by a radiologist with 15 years of experience.Field Strength/Sequence Data included 3 T MRI with T1- and T2-weighted sequences: dHCP (inversion recovery turbo spin-echo [IR-TSE] and turbo spin-echo [TSE]), BCP (magnetization-prepared rapid gradient-echo [MPRAGE] and TSE), and in-house (MPRAGE and variable-flip-angle TSE).Assessment The proposed approach was compared with alternative automated approaches trained with different labeling strategies. Training/validation/test splits were 100/25/25 (dHCP), 100/25/8 (BCP), and 50/10/10 (in-house).Statistical Tests Dice similarity coefficient (DSC), recall, positive predictive value, and Hausdorff distance were calculated for BG and BG-PVS quantification. Statistical significance was assessed using Wilcoxon signed-rank tests (p < 0.05), and quantification agreement was evaluated using Pearson&apos;s correlation, intraclass correlation coefficient (ICC), and mean absolute error (MAE).Results The proposed method improved accuracy (dHCP: BG DSC = 0.91 +/- 0.03 and BG-PVS DSC = 0.78 +/- 0.09; external datasets with fine-tuning: BG DSC = 0.86-0.89) and high agreement in PVS quantification with reference measurements (r = 0.90-0.99, ICC >= 0.96, MAE = 0.10).Data Conclusion The proposed method seems to enable robust and annotation-efficient BG and BG-PVS segmentation in infants.Evidence Level 3.Technical Efficacy 1.