ArticleInternational journal of computer assisted radiology and surgery2019
Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Comparison of Three Automated Measurements of Ventricular Volumes of the Brain.Journal of imaging informatics in medicine · 2026Article
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- Fully automated volumetry of ventricular subregions on computed tomography using object detection and semantic segmentation.Neuroimage. Reports · 2026Article
- Assessing CT-based volumetric analysis via deep learning for idiopathic normal pressure hydrocephalus.Brain communications · 2026Article
- 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
- Computational Modeling of Bridging Vein Rupture and Acute Subdural Hematoma Growth.Annals of biomedical engineering · 2025Article
- Automated landmark-based symmetric and standard alignment of skull base structures on CT.Neuroimage. Reports · 2025Article
- Longitudinal changes in ventricular volume after treating aqueduct stenosis through endoscopic third ventriculostomy in adults.Fluids and barriers of the CNS · 2025Article
- Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.International journal of computer assisted radiology and surgery · 2024Article
- Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.Radiology. Artificial intelligence · 2024Article
- Automated ventricular segmentation and shunt failure detection using convolutional neural networks.Scientific reports · 2024Article
- Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies.Frontiers in neuroimaging · 2023Article
- Prediction of shunt failure facilitated by rapid and accurate volumetric analysis: a single institution's preliminary experience.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2022Article
- AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus.Neural computing & applications · 2022Article
- Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach.Korean journal of radiology · 2021Article
- Application of Evans Index in Normal Pressure Hydrocephalus Patients: A Mini Review.Frontiers in aging neuroscience · 2021Review
- Artificial Intelligence in Neuroradiology: Current Status and Future Directions.AJNR. American journal of neuroradiology · 2020Article
- Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains.The British journal of radiology · 2020Article
- Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study.Frontiers in aging neuroscience · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeHydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations.
methodsA two-dimensional convolutional neural network was designed to perform fully automated ventricular segmentation on CT images. A total of 300 head CTs were collected and used in this exploration. Two hundred were used to train the network, 50 were used for validation, and 50 were used for testing.
resultsDice scores for the left lateral, right lateral, and third ventricle segmentations were 0.92, 0.92, and 0.79, respectively; the coefficients of determination were r
conclusionTwo-dimensional convolutional neural network architectures can be used to accurately segment and quantify intracranial ventricle volume. While further refinements are necessary, it is likely these networks could be used as a clinical tool to quantify hydrocephalus accurately and efficiently.
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