A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain

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, has provided significant biological insight into the pathogenesis of many neurological diseases affecting both adults and children. In this article, we present a review of applications of automated ventricular segmentation from magnetic resonance imaging (MRI) and offer a brief primer on brain segmentation methods to non-technical readers.

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Publication Details

Journal
Frontiers in Neurology
Published
2026-04-01
DOI
https://doi.org/10.3389/fneur.2025.1639381
Primary Topic
Medical Image Segmentation Techniques
Type
article
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article

A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain

Leopold Arko, Noam Harel, Carolina Sandoval-Garcia, Robert A. McGovern et al.
Frontiers in Neurology
Medical Image Segmentation Techniques
article

A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain

Leopold Arko, Noam Harel, Carolina Sandoval-Garcia, Robert A. McGovern, Birra Taha, Daniel Guillaume, Alexandra Benson
article en

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, has provided significant biological insight into the pathogenesis of many neurological diseases affecting both adults and children. In this article, we present a review of applications of automated ventricular segmentation from magnetic resonance imaging (MRI) and offer a brief primer on brain segmentation methods to non-technical readers.

Frontiers in NeurologyVol. 16
University of Minnesota (US), Normandale Community College (US), Resonance Research (United States) (US)
Openalex Percentile: Top 38%
Medical Image Segmentation Techniques
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