Comparison of Three Automated Measurements of Ventricular Volumes of the Brain

Accurate segmentation and measurement of ventricular volume are critical for neuroscience research and neurological disease diagnosis. In resource-limited settings, free and open-source automated tools offer accessible solutions. However, the lack of comparative evaluations limits their application. This study aims to identify reliable free tools for automated ventricular volume measurement to support clinical and research utilization. Magnetic resonance imaging (MRI) data from 150 healthy adults were collected with informed consent. Ventricular volumes were segmented using three open-source tools (3D Slicer, FreeSurfer, ITK-SNAP) and compared with manual segmentation as the reference standard. Pearson's correlation coefficient, intraclass correlation coefficient (ICC), and the Bland-Altman analysis were employed to evaluate consistency and reliability. All three automated tools showed significant correlations with manual measurements (P < 0.01). ITK-SNAP had the highest Pearson correlation and ICC values, followed by 3D Slicer, while FreeSurfer had the lowest. All tools demonstrated strong reliability, with ICCs greater than 0.9. The Bland-Altman analysis showed that ITK-SNAP had the closest consistency with manual results, again followed by 3D Slicer, with FreeSurfer performing least consistently. ITK-SNAP demonstrates higher accuracy and reliability for ventricular volumetry compared to 3D Slicer and FreeSurfer. Its open-source nature supports broader implementation in resource-constrained environments, enhancing neuroimaging accessibility for clinical and research applications.