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Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods

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 auto...

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Autores principales: Nowinski, Wieslaw L., Walecki, Jerzy, Półtorak-Szymczak, Gabriela, Sklinda, Katarzyna, Mruk, Bartosz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759129/
https://www.ncbi.nlm.nih.gov/pubmed/33391867
http://dx.doi.org/10.7717/peerj.10444
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author Nowinski, Wieslaw L.
Walecki, Jerzy
Półtorak-Szymczak, Gabriela
Sklinda, Katarzyna
Mruk, Bartosz
author_facet Nowinski, Wieslaw L.
Walecki, Jerzy
Półtorak-Szymczak, Gabriela
Sklinda, Katarzyna
Mruk, Bartosz
author_sort Nowinski, Wieslaw L.
collection PubMed
description 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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spelling pubmed-77591292020-12-31 Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods Nowinski, Wieslaw L. Walecki, Jerzy Półtorak-Szymczak, Gabriela Sklinda, Katarzyna Mruk, Bartosz PeerJ Neurology 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. PeerJ Inc. 2020-12-18 /pmc/articles/PMC7759129/ /pubmed/33391867 http://dx.doi.org/10.7717/peerj.10444 Text en ©2020 Nowinski et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Neurology
Nowinski, Wieslaw L.
Walecki, Jerzy
Półtorak-Szymczak, Gabriela
Sklinda, Katarzyna
Mruk, Bartosz
Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods
title Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods
title_full Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods
title_fullStr Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods
title_full_unstemmed Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods
title_short Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods
title_sort ischemic infarct detection, localization, and segmentation in noncontrast ct human brain scans: review of automated methods
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759129/
https://www.ncbi.nlm.nih.gov/pubmed/33391867
http://dx.doi.org/10.7717/peerj.10444
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