NeuroImage · 2026
Automated ventricle assessment via three-dimensional anatomical reconstruction (AVA-TAR): A computational toolkit for autonomous lateral ventricle assessment in preclinical hydrocephalus models.
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Chakladar Sundeep, Pan Shelei, Limbrick Owen, Pandey Maneesha, Halupnik Grace L, Zhao Annie, Mahjoub Moe R, Quirk James D, Nazeri Arash, Strahle Jennifer M
Аннотация
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 343 rodent brain MRIs (298 rats, 45 mice), each with manually segmented lateral ventricles serving as the ground truth. Model performance was evaluated using the Dice coefficient and 95th percentile Hausdorff distance (HD95). The most optimal model was evaluated further for its ability to quantify ventricle volume, convexity, surface area, and symmetry.
RESULTS: The U-Net++ model with an EfficientNet-B1 encoder achieved high accuracy (Dice: 0.819 ± 0.121; HD95: 2.493 ± 3.984). Further assessment of its morphological predictions found strong correlations with manual measurements of ventricular morphology, with Pearson and interclass correlation coefficients exceeding 0.95 across all metrics. The full validated pipeline was packaged into a publicly available application, hosted at https://ava-tar.org.
CONCLUSION: This study introduces a deep learning tool for automated segmentation and morphological analysis of lateral ventricles in rodent MRIs. The tool's efficiency and accuracy in quantifying ventricle morphology offers significant utility in preclinical hydrocephalus research with potential future application in the clinical setting.
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 343 rodent brain MRIs (298 rats, 45 mice), each with manually segmented lateral ventricles serving as the ground truth. Model performance was evaluated using the Dice coefficient and 95th percentile Hausdorff distance (HD95). The most optimal model was evaluated further for its ability to quantify ventricle volume, convexity, surface area, and symmetry.
RESULTS: The U-Net++ model with an EfficientNet-B1 encoder achieved high accuracy (Dice: 0.819 ± 0.121; HD95: 2.493 ± 3.984). Further assessment of its morphological predictions found strong correlations with manual measurements of ventricular morphology, with Pearson and interclass correlation coefficients exceeding 0.95 across all metrics. The full validated pipeline was packaged into a publicly available application, hosted at https://ava-tar.org.
CONCLUSION: This study introduces a deep learning tool for automated segmentation and morphological analysis of lateral ventricles in rodent MRIs. The tool's efficiency and accuracy in quantifying ventricle morphology offers significant utility in preclinical hydrocephalus research with potential future application in the clinical setting.