ArticleNeuroImage. Clinical2014
Automated delineation of stroke lesions using brain CT images.
Article in NeuroImage. Clinical, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses 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
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 139 citations in OpenAlex.
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- [Frontiers in medicine · 2024Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- 3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.Neuroinformatics · 2021Trial
- PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT.NeuroImage. Clinical · 2017Trial
- Tissue Is the Issue: A Systematic Review of Methods for the Determination of Infarct Volume in Acute Ischaemic Stroke.Brain sciences · 2025Review
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Taxonomy of Acute Stroke: Imaging, Processing, and Treatment.Diagnostics (Basel, Switzerland) · 2024Review
- Novel artificial intelligence-based hypodensity detection tool improves clinician identification of hypodensity on non-contrast computed tomography in stroke patients.Frontiers in neurology · 2024Article
- Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A comparison of lesion mapping analyses based on CT versus MR imaging in stroke.Neuropsychologia · 2023Article
- Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach.Scientific reports · 2022Article
- Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT.Clinical neuroradiology · 2022Article
- Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning.Biomedicines · 2022Article
- Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks.Annals of translational medicine · 2022Article
- Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.Computational and mathematical methods in medicine · 2022Article
- A Review on Computer Aided Diagnosis of Acute Brain Stroke.Sensors (Basel, Switzerland) · 2021Review
- Head CT: Toward Making Full Use of the Information the X-Rays Give.AJNR. American journal of neuroradiology · 2021Review
- Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives.International journal of environmental research and public health · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Computed tomographic (CT) images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner.
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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.