ArticleJournal of neurosurgery. Pediatrics2021
Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.
Article in Journal of neurosurgery. Pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Moving on in hydrocephalus imaging: from 2D to 3D biomarkers.Acta neurochirurgica · 2026Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Pediatric hydrocephalus and imaging evaluation of ventriculomegaly in the emergency setting.Pediatric radiology · 2026Review
- Comparison of Three Automated Measurements of Ventricular Volumes of the Brain.Journal of imaging informatics in medicine · 2026Article
- Application of deep neural networks in automatized ventriculometry and segmentation of the aqueduct in pediatric hydrocephalus patients.Neuroradiology · 2026Article
- Assessing CT-based volumetric analysis via deep learning for idiopathic normal pressure hydrocephalus.Brain communications · 2026Article
- Subcortical alterations following pediatric posterior fossa brain tumor treatment: A longitudinal MRI study.Neuroimage. Reports · 2025Article
- Automated Detection of Hydrocephalus in Pediatric Head Computed Tomography Using VGG 16 CNN Deep Learning Architecture and Based Automated Segmentation Workflow for Ventricular Volume Estimation.Journal of imaging informatics in medicine · 2025Article
- Imaging biomarkers for detection and longitudinal monitoring of ventricular abnormalities from birth to childhood.World journal of radiology · 2025Review
- Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients.Cancers · 2025Article
- From bench to bedside: murine models of inherited and sporadic brain arteriovenous malformations.Angiogenesis · 2025Review
- A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain.Frontiers in neurology · 2025Review
- Automated ventricular segmentation and shunt failure detection using convolutional neural networks.Scientific reports · 2024Article
- An international study presenting a federated learning AI platform for pediatric brain tumors.Nature communications · 2024Article
- Volumetric changes in temporomandibular joint space following trans-oral vertical ramus osteotomy in patients with mandibular prognathism: a one-year follow-up study.Scientific reports · 2024Article
- Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery.Advances in experimental medicine and biology · 2024Review
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 2 countries.
Funding
Abstract
objectiveImaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.
methodsThe study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.
resultsModel segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).
conclusionsThe authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.
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