MINI REVIEW article
Front. Neurol.
Sec. Applied Neuroimaging
A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain
Birra Taha 1
Alexandra Benson 2
Leopold Arko 1
Noam Harel 3
Carolina Sandoval-Garcia 1
Daniel Guillaume 1
Robert McGovern 1
1. Department of Neurosurgery, University of Minnesota, Minneapolis, United States
2. Normandale Community College, Bloomington, United States
3. University of Minnesota Twin Cities Center for Magnetic Resonance Research, Minneapolis, United States
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Abstract
Ventricular parcellation or segmentation is the systematic assignment of pixels (or voxels), from an image of the brain, to the ventricular compartment. As opposed to manual methods, automated techniques seek to streamline segmentation for better, objective delineation of the ventricles. The refinement of these methods, powered by advances in computer vision, have provided significant biological insight into the pathogenesis of many neurological diseases affecting both adults and children. In this article, we present a review into applications of automated ventricular segmentation from magnetic resonance imaging (MRI) and offer a brief primer on brain segmentation methods to non-technical readers.
Summary
Keywords
Ventricles, Hydrocephalus, Computer Vision, segmentation, deep learning
Received
02 June 2025
Accepted
05 December 2025
Copyright
© 2025 Taha, Benson, Arko, Harel, Sandoval-Garcia, Guillaume and McGovern. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Birra Taha
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.