Developing Deep Learning-Based Cerebral Ventricle Auto-Segmentation System and Clinical Application for the...

Current methods for evaluating ventriculomegaly, particularly Evans' Index (EI), fail to accurately assess three-dimensional ventricular changes. We developed and validated an automated multiclass segmentation system for precise volumetric assessment, simultaneously segmenting 5 anatomical classes (ventricles, parenchyma, skull, skin, and hemorrhage) to support future augmented reality-guided external ventricular drainage (EVD) systems.

Using the nnUNet architecture, we trained our model on 288 brain computed tomography scans with diverse pathological conditions and validated it using internal (n = 10), external (n = 43), and public (n = 192) datasets. Clinical validation involved 227 patients who underwent cerebrospinal fluid (CSF) drainage procedures. We compared automated volumetric measurements against traditional EI measurements and actual CSF drainage volumes in surgical cases.

The model achieved exceptional performance with a mean Dice similarity coefficient of 93.0% across all 5 classes, demonstrating consistent performance across institutional and public datasets, with particularly robust ventricle segmentation (92.5%). Clinical validation revealed EI was the strongest single predictor of ventricular volume (adjusted R² = 0.430; P < 0.001), though influenced by age, sex, and diagnosis type. Most significantly, in EVD cases, automated volume differences showed remarkable correlation with actual CSF drainage amounts (β = 0.956; adjusted R² = 0.936; P < 0.001), validating the system's accuracy in measuring real CSF volume changes.

Our comprehensive multiclass segmentation system offers a superior alternative to traditional measurements with potential for noninvasive CSF dynamics monitoring and augmented reality-guided EVD placement.