A machine learning approach to the morphology of human brain ventricles

Abstract: Brain ventricular morphology contains crucial pathological information for neurological conditions including Alzheimer's disease, Parkinson's disease, and multiple sclerosis. However, manual segmentation from MRI scans is time-consuming (3-4 hours) and limits large-scale analysis. This work presents a machine learning pipeline for automated ventricular analysis. We developed an Attention U-Net-based model achieving state-of-the-art segmentation in 20 seconds, alongside a semi-automatic approach that reduces manual segmentation time by 10-fold while maintaining quality. We extended this into a complete pipeline for large-scale morphological analysis using anatomical landmarks and graph-based learning on point clouds. Leveraging the UK Biobank dataset (~100,000 MRI scans, including 1,000 disease samples), we demonstrate scalable population-level analysis. The pipeline maintains interpretability, enabling clinicians to understand the morphological features driving predictions, bridging deep learning methodology with clinical neuroimaging applications.