ArticlePeerJ2020
Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Fully automated quantification of net water uptake in acute ischemic stroke using only non-contrast CT imaging.European radiology · 2026Article
- Cerebral ischemia detection using deep learning techniques.Health information science and systems · 2025Article
- Taxonomy of Acute Stroke: Imaging, Processing, and Treatment.Diagnostics (Basel, Switzerland) · 2024Review
- Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT.Journal of neurointerventional surgery · 2024Article
- Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model.Theranostics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.
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Registered trials
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