![]() ![]() 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. 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. ![]() 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. Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods. Cite this article Nowinski WL, Walecki J, Półtorak-Szymczak G, Sklinda K, Mruk B. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 2 Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland DOI 10.7717/peerj.10444 Published Accepted Received Academic Editor Gaurav Sharma Subject Areas Neurology, Radiology and Medical Imaging, Science and Medical Education, Computational Science Keywords Ischemic stroke, Human brain, Detection, Localization, Segmentation, Noncontrast CT, Brain atlas, Image processing, Image analysis, Artificial intelligence, Review Copyright © 2020 Nowinski et al. ![]()
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