Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-o...

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Autores principales:	Sungjin Lee, Beanbonyka Rim, Sung-Shick Jou, Hyo-Wook Gil, Xibin Jia, Ahyoung Lee, Min Hong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	calcium detection coronary artery calcium score CT resnet-50 VGG inception resnet V2 deep learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/acf5620e3b6543e8a480f1f167d1fd2d
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spelling	oai:doaj.org-article:acf5620e3b6543e8a480f1f167d1fd2d2021-11-11T19:04:57ZDeep-Learning-Based Coronary Artery Calcium Detection from CT Image10.3390/s212170591424-8220https://doaj.org/article/acf5620e3b6543e8a480f1f167d1fd2d2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7059https://doaj.org/toc/1424-8220One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.Sungjin LeeBeanbonyka RimSung-Shick JouHyo-Wook GilXibin JiaAhyoung LeeMin HongMDPI AGarticlecalcium detectioncoronary artery calcium score CTresnet-50VGGinception resnet V2deep learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7059, p 7059 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	calcium detection coronary artery calcium score CT resnet-50 VGG inception resnet V2 deep learning Chemical technology TP1-1185
spellingShingle	calcium detection coronary artery calcium score CT resnet-50 VGG inception resnet V2 deep learning Chemical technology TP1-1185 Sungjin Lee Beanbonyka Rim Sung-Shick Jou Hyo-Wook Gil Xibin Jia Ahyoung Lee Min Hong Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
description	One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
format	article
author	Sungjin Lee Beanbonyka Rim Sung-Shick Jou Hyo-Wook Gil Xibin Jia Ahyoung Lee Min Hong
author_facet	Sungjin Lee Beanbonyka Rim Sung-Shick Jou Hyo-Wook Gil Xibin Jia Ahyoung Lee Min Hong
author_sort	Sungjin Lee
title	Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
title_short	Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
title_full	Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
title_fullStr	Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
title_full_unstemmed	Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
title_sort	deep-learning-based coronary artery calcium detection from ct image
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/acf5620e3b6543e8a480f1f167d1fd2d
work_keys_str_mv	AT sungjinlee deeplearningbasedcoronaryarterycalciumdetectionfromctimage AT beanbonykarim deeplearningbasedcoronaryarterycalciumdetectionfromctimage AT sungshickjou deeplearningbasedcoronaryarterycalciumdetectionfromctimage AT hyowookgil deeplearningbasedcoronaryarterycalciumdetectionfromctimage AT xibinjia deeplearningbasedcoronaryarterycalciumdetectionfromctimage AT ahyounglee deeplearningbasedcoronaryarterycalciumdetectionfromctimage AT minhong deeplearningbasedcoronaryarterycalciumdetectionfromctimage
_version_	1718431665288642560

Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

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