Cargando…

COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net

Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task...

Descripción completa

Detalles Bibliográficos
Autores principales: Ilhan, Ahmet, Alpan, Kezban, Sekeroglu, Boran, Abiyev, Rahib
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9886330/
https://www.ncbi.nlm.nih.gov/pubmed/36743788
http://dx.doi.org/10.1016/j.procs.2023.01.144
_version_ 1784880111556755456
author Ilhan, Ahmet
Alpan, Kezban
Sekeroglu, Boran
Abiyev, Rahib
author_facet Ilhan, Ahmet
Alpan, Kezban
Sekeroglu, Boran
Abiyev, Rahib
author_sort Ilhan, Ahmet
collection PubMed
description Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging. The researchers investigated image preprocessing techniques to accomplish this problem and to enable more accurate segmentation by the AI-based systems. This study proposes a COVID-19 Lung-CT segmentation system based on histogram-based non-parametric region localization and enhancement (LE) methods prior to the U-Net architecture. The COVID-19-infected lung CT images were initially processed by the LE method, and the infected regions were detected and enhanced to provide more discriminative features to the deep learning segmentation methods. The U-Net is trained using the enhanced images to segment the regions affected by COVID-19. The proposed system achieved 97.75%, 0.85, and 0.74 accuracy, dice score, and Jaccard index, respectively. The comparison results suggested that the use of LE methods as a preprocessing step in CT Lung images significantly improved the feature extraction and segmentation abilities of the U-Net model by a 0.21 dice score. The results might lead to implementing the LE method in segmenting varied medical images.
format Online
Article
Text
id pubmed-9886330
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher The Author(s). Published by Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-98863302023-01-31 COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net Ilhan, Ahmet Alpan, Kezban Sekeroglu, Boran Abiyev, Rahib Procedia Comput Sci Article Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging. The researchers investigated image preprocessing techniques to accomplish this problem and to enable more accurate segmentation by the AI-based systems. This study proposes a COVID-19 Lung-CT segmentation system based on histogram-based non-parametric region localization and enhancement (LE) methods prior to the U-Net architecture. The COVID-19-infected lung CT images were initially processed by the LE method, and the infected regions were detected and enhanced to provide more discriminative features to the deep learning segmentation methods. The U-Net is trained using the enhanced images to segment the regions affected by COVID-19. The proposed system achieved 97.75%, 0.85, and 0.74 accuracy, dice score, and Jaccard index, respectively. The comparison results suggested that the use of LE methods as a preprocessing step in CT Lung images significantly improved the feature extraction and segmentation abilities of the U-Net model by a 0.21 dice score. The results might lead to implementing the LE method in segmenting varied medical images. The Author(s). Published by Elsevier B.V. 2023 2023-01-31 /pmc/articles/PMC9886330/ /pubmed/36743788 http://dx.doi.org/10.1016/j.procs.2023.01.144 Text en © 2023 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Ilhan, Ahmet
Alpan, Kezban
Sekeroglu, Boran
Abiyev, Rahib
COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
title COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
title_full COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
title_fullStr COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
title_full_unstemmed COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
title_short COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
title_sort covid-19 lung ct image segmentation using localization and enhancement methods with u-net
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9886330/
https://www.ncbi.nlm.nih.gov/pubmed/36743788
http://dx.doi.org/10.1016/j.procs.2023.01.144
work_keys_str_mv AT ilhanahmet covid19lungctimagesegmentationusinglocalizationandenhancementmethodswithunet
AT alpankezban covid19lungctimagesegmentationusinglocalizationandenhancementmethodswithunet
AT sekerogluboran covid19lungctimagesegmentationusinglocalizationandenhancementmethodswithunet
AT abiyevrahib covid19lungctimagesegmentationusinglocalizationandenhancementmethodswithunet