Automatic Estimation of Neonatal Ventricles-to-Brain Volume Ratio using AI for Monitoring Hydrocephalus

Motivation: To propose a new approach for managing hydrocephalus in neonates based on neonatal MRI and AI-based brain ventricle volume quantitation, brain parenchyma volume and ventricle-to-brain volumes ratio. Goal(s): To evaluate the robustness of quantitative measurements of ventricle and brain volumes using AI-based algorithms, on scans acquired on a 1 Tesla permanent magnet neonatal MRI. Approach: The performance of three AI-based segmentation algorithms was evaluated using linear correlations and Intersection over Union (IoU) score between ground truth and predictions. Results: Results show high linear correlations between ground truth and algorithm predictions, validating the use of these volumetric measurements to monitor hydrocephalus longitudinally. Impact: An AI based method for segmenting neonatal MRI images may provide volumetric quantitation and enable fast and accurate decision making on surgical intervention in preterm infants with hydrocephalus.