Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images

Covid-19 disease has confronted the world with an unprecedented health crisis, faced with its quick spread, the health system is called upon to increase its vigilance. So, it is essential to set up a quick and automated diagnosis that can alleviate pressure on health systems. Many techniques used to...

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Autores principales:	Mohamed Chala, Nsiri Benayad, Abdelmajid Soulaymani, Abdelghani Mokhtari, Brahim Benaji
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	vigilance decision support convolutional neural network image segmentation covid-19 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/4480b8ac16944ceba9e47a248aa1196e
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spelling	oai:doaj.org-article:4480b8ac16944ceba9e47a248aa1196e2021-11-12T11:44:08ZHealth Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images2267-124210.1051/e3sconf/202131901089https://doaj.org/article/4480b8ac16944ceba9e47a248aa1196e2021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/95/e3sconf_vigisan_01089.pdfhttps://doaj.org/toc/2267-1242Covid-19 disease has confronted the world with an unprecedented health crisis, faced with its quick spread, the health system is called upon to increase its vigilance. So, it is essential to set up a quick and automated diagnosis that can alleviate pressure on health systems. Many techniques used to diagnose the covid-19 disease, including imaging techniques, like computed tomography (CT). In this paper, we present an automatic method for COVID-19 Lung Infection Segmentation from CT Images, that can be integrated into a decision support system for the diagnosis of covid-19 disease. To achieve this goal, we focused to new techniques based on artificial intelligent concept, in particular the uses of deep convolutional neural network, and we are interested in our study to the most popular architecture used in the medical imaging community based on encoder-decoder models. We use an open access data collection for Artificial Intelligence COVID-19 CT segmentation or classification as dataset, the proposed model implemented on keras framework in python. A short description of model, training, validation and predictions is given, at the end we compare the result with an existing labeled data. We tested our trained model on new images, we obtained for Area under the ROC Curve the value 0.884 from the prediction result compared with manual expert segmentation. Finally, an overview is given for future works, and use of the proposed model into homogeneous framework in a medical imaging context for clinical purpose.Mohamed ChalaNsiri BenayadAbdelmajid SoulaymaniAbdelghani MokhtariBrahim BenajiEDP Sciencesarticlevigilancedecision supportconvolutional neural networkimage segmentationcovid-19Environmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 319, p 01089 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	vigilance decision support convolutional neural network image segmentation covid-19 Environmental sciences GE1-350
spellingShingle	vigilance decision support convolutional neural network image segmentation covid-19 Environmental sciences GE1-350 Mohamed Chala Nsiri Benayad Abdelmajid Soulaymani Abdelghani Mokhtari Brahim Benaji Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images
description	Covid-19 disease has confronted the world with an unprecedented health crisis, faced with its quick spread, the health system is called upon to increase its vigilance. So, it is essential to set up a quick and automated diagnosis that can alleviate pressure on health systems. Many techniques used to diagnose the covid-19 disease, including imaging techniques, like computed tomography (CT). In this paper, we present an automatic method for COVID-19 Lung Infection Segmentation from CT Images, that can be integrated into a decision support system for the diagnosis of covid-19 disease. To achieve this goal, we focused to new techniques based on artificial intelligent concept, in particular the uses of deep convolutional neural network, and we are interested in our study to the most popular architecture used in the medical imaging community based on encoder-decoder models. We use an open access data collection for Artificial Intelligence COVID-19 CT segmentation or classification as dataset, the proposed model implemented on keras framework in python. A short description of model, training, validation and predictions is given, at the end we compare the result with an existing labeled data. We tested our trained model on new images, we obtained for Area under the ROC Curve the value 0.884 from the prediction result compared with manual expert segmentation. Finally, an overview is given for future works, and use of the proposed model into homogeneous framework in a medical imaging context for clinical purpose.
format	article
author	Mohamed Chala Nsiri Benayad Abdelmajid Soulaymani Abdelghani Mokhtari Brahim Benaji
author_facet	Mohamed Chala Nsiri Benayad Abdelmajid Soulaymani Abdelghani Mokhtari Brahim Benaji
author_sort	Mohamed Chala
title	Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images
title_short	Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images
title_full	Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images
title_fullStr	Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images
title_full_unstemmed	Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images
title_sort	health vigilance for medical imaging diagnostic optimization: automated segmentation of covid-19 lung infection from ct images
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/4480b8ac16944ceba9e47a248aa1196e
work_keys_str_mv	AT mohamedchala healthvigilanceformedicalimagingdiagnosticoptimizationautomatedsegmentationofcovid19lunginfectionfromctimages AT nsiribenayad healthvigilanceformedicalimagingdiagnosticoptimizationautomatedsegmentationofcovid19lunginfectionfromctimages AT abdelmajidsoulaymani healthvigilanceformedicalimagingdiagnosticoptimizationautomatedsegmentationofcovid19lunginfectionfromctimages AT abdelghanimokhtari healthvigilanceformedicalimagingdiagnosticoptimizationautomatedsegmentationofcovid19lunginfectionfromctimages AT brahimbenaji healthvigilanceformedicalimagingdiagnosticoptimizationautomatedsegmentationofcovid19lunginfectionfromctimages
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Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images

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