Automated Ventricle Assessment via Three-dimensional Anatomical Reconstruction (AVA-TAR): a computational toolkit for autonomous lateral ventricle assessment in preclinical hydrocephalus models.

ABSTRACT:

Introduction

Current workflows for studying hydrocephalus in rodent models rely on manual segmentation or qualitative assessment of ventricular size on small animal magnetic resonance imaging, which are both inefficient and prone to variability. Atlas-based methods enable more streamlined segmentation, but their analysis is limited to morphologically normal samples.

Objective

This study aimed to develop and internally validate a deep learning model that performs automated segmentation of lateral ventricles in rodent brain MRIs, allowing for 3D ventricle reconstruction, morphological analysis, and ventriculomegaly detection.

Methods

Four U-Net++ neural networks, each with different encoder backbones, were trained using 307 rodent brain MRIs (262 rats, 45 mice), each wi

SUBMITTER: Chakladar S 

PROVIDER: S-EPMC12889625 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature