Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net

Abstract Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient’s infection, thereby speeding...

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Autores principales:	Qin Zhang, Xiaoqiang Ren, Benzheng Wei
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f4a332f3fd3a4521a1d8c50c1117603d
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spelling	oai:doaj.org-article:f4a332f3fd3a4521a1d8c50c1117603d2021-11-28T12:19:35ZSegmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net10.1038/s41598-021-01502-02045-2322https://doaj.org/article/f4a332f3fd3a4521a1d8c50c1117603d2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01502-0https://doaj.org/toc/2045-2322Abstract Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient’s infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.Qin ZhangXiaoqiang RenBenzheng WeiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qin Zhang Xiaoqiang Ren Benzheng Wei Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net
description	Abstract Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient’s infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.
format	article
author	Qin Zhang Xiaoqiang Ren Benzheng Wei
author_facet	Qin Zhang Xiaoqiang Ren Benzheng Wei
author_sort	Qin Zhang
title	Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net
title_short	Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net
title_full	Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net
title_fullStr	Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net
title_full_unstemmed	Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net
title_sort	segmentation of infected region in ct images of covid-19 patients based on qc-hc u-net
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f4a332f3fd3a4521a1d8c50c1117603d
work_keys_str_mv	AT qinzhang segmentationofinfectedregioninctimagesofcovid19patientsbasedonqchcunet AT xiaoqiangren segmentationofinfectedregioninctimagesofcovid19patientsbasedonqchcunet AT benzhengwei segmentationofinfectedregioninctimagesofcovid19patientsbasedonqchcunet
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Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net

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