Abstract
Ventriculomegaly is a key neuroimaging feature in conditions such as normal pressure hydrocephalus (NPH) and other disorders of cerebrospinal fluid (CSF) dynamics. The Evans Index (EI), defined as the ratio of the maximal frontal horn width to the maximal inner skull diameter, remains a simple and widely used marker for ventricular enlargement. However, manual EI measurement is subject to observer variability and dependent on proper alignment to the anterior commissure-posterior commissure (AC-PC) plane, limiting reproducibility in large-scale and multi-center studies. We present a fully automated deep learning-based pipeline for EI calculation directly from raw T1-weighted MPRAGE MRI scans. The pipeline integrates (i) landmark detection using the BrainSignsNet model, (ii) rigid AC-PC alignment, and (iii) robust segmentation of the lateral ventricles (LV) and intracranial volume (ICV) via nnU-Net models, including a custom ventricular network trained on 1,300 annotated scans enriched for hydrocephalus. The Evans Index is then derived from automated measurement of frontal horn width and inner skull diameter at the aligned axial slice. Internal validation using data from the Baltimore Longitudinal Study of Aging, BIOCARD, and Johns Hopkins cohorts demonstrated high segmentation accuracy (Dice coefficient = 0.98). External validation on the PENS trial, including pre- and post-shunt NPH scans, showed excellent agreement with expert manual EI measurements (mean bias = 0.0068, mean absolute error = 0.0103, r = 0.96, p < 0.001). Bias analyses revealed no significant association between measurement error and age, sex, or ventricular volume This fully automated, orientation-standardized method achieves accurate and reproducible Evans Index measurement across diverse MRI datasets. By eliminating manual intervention, the pipeline enhances scalability for large neuroimaging cohorts and provides a reliable tool for clinical assessment, screening and monitoring of ventriculomegaly, especially in NPH.