AIMC Topic: Cerebral Ventricles
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Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.
Journal of neurosurgery. Pediatrics
Dec 1, 2020
OBJECTIVE: Imaging 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...
Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains.
The British journal of radiology
Jan 3, 2020
OBJECTIVE: CT is the mainstay imaging modality for assessing change in ventricular volume in patients with ventricular shunts or external ventricular drains (EVDs). We evaluated the performance of a novel fully automated CT registration and subtracti...
Comparison of morphometric parameters in prediction of hydrocephalus using random forests.
Computers in biology and medicine
Nov 20, 2019
Ventricles of the human brain enlarge with aging, neurodegenerative diseases, intrinsic, and extrinsic pathologies. The morphometric examination of neuroimages is an effective approach to assess structural changes occurring due to diseases such as hy...
Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.
International journal of computer assisted radiology and surgery
Jul 26, 2019
PURPOSE: Hydrocephalus 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...
Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly.
NeuroImage. Clinical
May 24, 2019
Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathog...
Automatically measuring brain ventricular volume within PACS using artificial intelligence.
PloS one
Mar 15, 2018
The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and...
Learning and combining image neighborhoods using random forests for neonatal brain disease classification.
Medical image analysis
Aug 9, 2017
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensiona...
Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement.
Neurosurgical focus
Jul 1, 2025
OBJECTIVE: The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI.
Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy.
Radiology. Artificial intelligence
May 1, 2024
Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materi...
[[Applications] 13. Segmentation of Infant Brain Ventricles with Hydrocephalus Using Deep Learning].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2024
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