ArticleScientific reports2017
White Matter and Gray Matter Segmentation in 4D Computed Tomography.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.PloS one · 2025Article
- Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT.BMC medical imaging · 2022Article
- A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages.AJNR. American journal of neuroradiology · 2022Article
- Feasibility study of portable multi-energy computed tomography with photon-counting detector for preclinical and clinical applications.Scientific reports · 2021Article
- Integrating regional perfusion CT information to improve prediction of infarction after stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT.Frontiers in computational neuroscience · 2021Article
- Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.Radiology. Artificial intelligence · 2020Article
- Creation of an anthropomorphic CT head phantom for verification of image segmentation.Medical physics · 2020Article
- A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease.AJNR. American journal of neuroradiology · 2020Article
- Radiological images and machine learning: Trends, perspectives, and prospects.Computers in biology and medicine · 2019Review
- Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions.Frontiers in neuroinformatics · 2019Article
- Robust Segmentation of the Full Cerebral Vasculature in 4D CT of Suspected Stroke Patients.Scientific reports · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.