Automated Cerebrospinal Fluid Flow Quantification in Hydrocephalus Using Phase-Contrast Magnetic Resonance Imaging

Quantitative cerebrospinal fluid (CSF) flow analysis with phase-contrast magnetic resonance imaging depends on segmentation and is affected by operator variability. This cohort method-comparison study evaluated whether a flow-aware deep learning (DL) model provides reproducible C2-C3 CSF segmentation and flow quantification compared with repeated manual segmentation. Ten hydrocephalus patients underwent 3-T phase-contrast magnetic resonance imaging at C2-C3, with 32 magnitude and phase frames across the cardiac cycle. Three experts and one novice segmented each case twice using semi-automatic software, generating 80 manual annotations. A pretrained flow-aware 2D-UNet generated one fixed automatic segmentation per case. The inference pipeline was deterministic, meaning that repeated processing of the same input with fixed model weights and settings produced identical masks. A multi-expert reference was defined as the arithmetic mean of the expert measurements. Region of interest (ROI) surface area, peak flow amplitude, and stroke volume were compared using mean difference, Bland-Altman 95% limits of agreement, Lin's concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC). Expert ICC(1,1) was 0.88-0.97 for surface area, 0.97-1.00 for peak flow amplitude, and 0.97-0.99 for stroke volume; novice values were 0.63, 0.95, and 0.92, respectively. Compared with the multi-expert reference, model mean differences were -0.42 mm² for surface area, -0.00 mL/s for peak flow amplitude, and -0.01 mL for stroke volume, with CCC values of 0.50, 0.87, and 0.86. Bland-Altman limits of agreement were -69.12 to 68.27 mm², -1.47 to 1.46 mL/s, and -0.34 to 0.31 mL. The model provided rapid, deterministic CSF segmentation with small cohort-level mean differences in peak flow amplitude and stroke volume; however, individual-level agreement with the flow-derived multi-expert reference was lower than for expert and novice segmentations obtained using the semi-automatic workflow.